Summary with the 3rd edition of Operations and supply chain management by Jacobs and Chase

Chapter 1

Operations and Supply Chain Management (OSCM) is the design, operation, and improvement of the systems that create and deliver the firm’s primary products and services. OCSM is concerned with the management of the entire system that produces a product or delivers a service. For every product/service a supply network as shown in exhibit 1.1 can be made.

Success in today’s global markets requires a business strategy that matches the preferences of customers with customers, networks, shareholders, employees and the environment (see also exhibit 1.2). Operations refers to manufacturing and service processes that are used to transform the resources employed by a firm into products desired by customers. A Supply Chain encompasses all activities associated with the flow and transformation of goods and services from the raw materials stage through to the end-user, as well as the associated information flows.

A process is made up of one or more activities that transform inputs into outputs. Operations and supply chain processes can be categorized as follows:

• Planning: processes needed to operate an existing supply chain strategically;

• Sourcing: selection of suppliers that will deliver the goods and services needed to create the firm’s product;

• Making: where the product is produced or the service is provided;

• Delivering: also logistics processes. Delivering products to warehouses and customers, contact with customers and information systems need to be managed;

• Returning: involves the processes for receiving worn-out, defective, and excess products back from customers and support for customers who have problems with the delivered product.

The are five essential differences between goods and services:

• Intangible process: a service cannot be weighed or measured. This means that services innovations cannot be patented and customers cannot try the service beforehand.

• Requires the degree of interaction with the customer to be a service;

• Heterogeneous: services vary day to day between the customer and the servers, whereas variation in producing goods can be almost zero;

• Perishable and time dependent: services can’t be stored;

• Specification of a services can be defined as a package of features that affect the five senses, existing of supporting facility like location and layout, facilitating goods like variety, consistency and quantity, explicit services like training of the personnel and availability to the service, and implicit service like attitude of the personnel, waiting time and privacy.

The Goods-Services Continuum is provided in exhibit 1.4. Learn this! Product-service bundling refers to when a firm builds service activities into its product offerings to create additional value for the customer.

Efficiency Doing something at the lowest possible cost.

Effectiveness Doing the things that will create most value for the customer.

Value The attractiveness of a product relative to its cost.

An interesting relation between OCSM functions and profit is the direct impact of a cost reduction in one of these functions on the profit margin. There are two ratios that relate to the productivity of labour employed by the firm: net income per employee and revenue (or sales) per employee. The receivables turnover ratio measures the efficiency of a company in collecting its sales on credit:

Receivable turnover = annual credit sales / average account receivable

The lower the ratio, the longer receivables are being held and the higher the risk of them not being collected. Another ratio is the inventory turnover, which measures the average number of times inventory is sold and replaced during the fiscal year. This ratio measures how efficient the company turns its inventory into sales.

Inventory turnover = costs of goods sold / average inventory value

The asset turnover ratio is the amount of sales generated for every dollar’s worth of assets. This measures how efficient a firm is using its assets in generating sales revenue.

Asset turnover = revenue (or sales) / total assets

The historical development of OCSM (see also exhibit 1.7):

1. The Manufacturing Strategy Paradigm was developed in the late 1970s and early 1980s by researchers at the Harvard Business School;

2. Lean manufacturing, Just-In-Time and Total Quality Management became in the 1980s popular by the Japanese;

3. Standardized service quality and productivity became popular by McDonald;

4. Total Quality Management and Quality Certification (e.g., ISO 9000) developed in the late 1980s and 1990s by Deming, Juran and Crosby;

5. Business Process Reengineering (revolutionairy instead of evolutionairy changes) was necessary in the economic recession in the 1990s and was described by Michael Hammer. Taylor (scientific management) and Frank and Lilian Gilbreth were also important in this field;

6. Six Sigma Quality tools were extended in the 1990s and can be used in many different organisations;

7. Supply Chain Management is a total system approach to managing the flow of information, materials and services from material suppliers through factories and warehouses to the end customer. Mass customization is the ability to produce a unique product exactly to a particular customer’s requirements;

8. Electronic Commere became popular in the late 1990s because of the rise of Internet and the use of it as an essential element of business activity;

9. Service science was a direct response to the growth of services;

10. Business analytics is the use of current business data to solve business problems using mathematical analysis.

As operations and supply management is a dynamic field, a global enterprise challenges nowadays different issues:

1. Coordination between mutually supportive but separate organizations (existence of contract manufacturers that are specialized in performing focused manufacturing activities);

2. Optimization of global supplier, production, and distribution networks;

3. Management of customer touch points (recognition that making resource utilization decisions must capture the implicit costs of lost customers as well as the direct costs of staffing);

4. Raising senior management awareness of operations as a significant competitive weapon;

5. Sustainability and the triple bottom line: sustainability is the ability to meet current resource needs without compromising the ability of future generations to meet their needs. Triple bottom line is a business strategy that includes social, economic, and environmental criteria.

Chapter 2

The strategy describes how a firm creates and sustains value for its current shareholders. By adding sustainability, future generations are taken into account.

Shareholders Own one or more shares of stock in the company.

Stakeholders Indirectly or directly influenced by the activities of the firm.

Firms have more and more focus on stakeholders. The Triple Bottom Line captures an expanded spectrum of values by evaluating a firm against the following criteria (see also exhibit 2.1):

• Social: pertains to fair and beneficial business practices toward labour, the community, and the region in which a firm conducts is business;

• Economic: the firm’s obligation to compensate shareholders who provide capital via competitive returns on investment;

• Environmental: the firm’s impact on the environment and society at large.

Operations and supply chain strategy is the setting of broad policies and plans for using the firm’s resources optimally. This must be integrated with corporate strategy.
Operations effectiveness is performing activities in a manner that best implements strategic priorities at minimum cost.

A planning strategy involves a set of repeating activities, which are performed in different time intervals and in a closed-loop process (see exhibit 2.2):

• Develop/Refine the Strategy (yearly):

  • Define vision, mission and objectives

  • Conduct strategic analysis

  • Define strategy competitive priorities

• Translate the Strategy (quarterly):

  • Product design/revision initiatives

  • Sourcing/location of facilities initialization

• Major Focus Points and Projects:

  • Major operations initiatives

  • Major logistics/distribution initiatives

There are seven major competitive dimensions forming the competitive position of a firm:

1. Cost or price: the choice to either make the product or deliver the service cheap;

2. Quality: the firm’s definition of how the product or service is to be made;

3. Delivery speed: the firm’s ability to make the product or deliver the service quickly;

4. Delivery reliability: the firm’s ability to deliver the product when promised;

5. Coping with changes in demand: the firm’s ability to respond to the change in demand;

6. Flexibility and New-Product introduction speed: the firm’s ability to be flexible in order to offer a wide variety of production to its customers in a given time;

7. Other product-specific criteria relate to specific products or situations. Special services can increase sales of manufactured products such as technical liaison and support, meeting a launch date, supplier after-sale support, environmental impact and other dimensions (e.g. color, size, weight).

Companies cannot be good in everything. Trade-offs occur. A trade-off occurs when activities are incompatible so that more of one thing necessitates less of another (e.g. high quality is viewed as a trade-off to low cost). Straddling occurs when a company seeks to match the benefits of a successful position while maintaining its existing position.

Order winner is a specific marketing-orientated dimension that clearly differentiates a product from competing products. This dimension can be price, quality or reliability. An order qualifier is a dimension used to screen a product or service as a candidate for purchase, for example the battery life of a new computer.

All activities that make up a firm’s operation relate to one another. Activity- system maps are diagrams that show how a company’s strategy is delivered through a set of supporting activities (see exhibit 2.3).

The management of risk is crucial for OCSM. Supply chain risk is the likelihood of a disruption that would impact the ability of a company to continuously supply products or services. Supply chain disruptions are unplanned and unanticipated events that disrupt the normal flow of goods and materials within a supply chain. There are two dimensions of these events: (1) supply chain coordination risks that are associated with the day-to-day management of the supply chain and (2) disruption risks, which are caused by natural or manmade disasters, such as earthquakes, hurricanes and terrorism. There are three steps in the risk management process that can be applied to situations where disruptions are possible:

1. Identify the sources of potential disruptions;

2. Assess the potential impact of the risk;

3. Develop plans to mitigate the risk.

Productivity is a measure of how well resources are used. The formula:

Productivity = outputs / inputs

To increase productivity, this ratio must be as large as practical. However, productivity is a relative measure and should always be compared to something else. This could be competitors or the process over time. Exhibit 2.5 gives examples of productivity measures.

Chapter 3

Forecasting is essential for every organisation and is the basis of corporate long-rung planning. Strategic forecasts are medium and long-term forecasts that are used for decisions related to strategy and aggregate demand. Tactical forecasts are short-term forecasts used for making day-to-day decisions related to meeting demand.

There are four basis types of forecasting: qualitative, time series analysis, causal relationships and simulation. Qualitative techniques are subjective and based on estimates and opinions Time series analysis (focus of this chapter) is a forecast in which past demand data is used to predict future demand. For causal forecasting the linear regression technique is used, which assumes that demand is related to some underlying factor(s) in the environment. Simulation models allow the forecaster to run through a range of assumptions about the condition of the forecast.

Demand for products/services can be broken down into six components in most cases: average demand for the period, a trend, seasonal element, cyclical elements, random variation and autocorrelation (see exhibit 3.1). Cyclical influence on demand may come from occurrences as political elections, war or economic conditions. Random variation is caused by chance events. Autocorrelation denotes the persistence of occurrence. Trend lines are the usual starting point in developing a forecast. Exhibit 3.2 illustrates the four common types of trends.

Time series forecasting models try to predict the future based on past data. Exhibit 3.3 shows the time series models of this chapter. Short-term refers to under three months, medium term to three months to two years and long-term to greater than two years. Which model a company uses depends on the time horizon to forecast, data availability, the required accuracy, the size of the forecasting budget and the availability of qualified personnel. Other issues as the firm’s degree of flexibility are also important. The consequence of a bad forecast also needs to be taken into consideration.

When the demand for a product is constant, and there are no seasonal characteristics, the moving average can be used: a forecast based on average past demand. See exhibit 3.4 for an example. Selecting the period length should be dependent on how the forecast is going to be used. The formula:

Ft= (At-1+ At-2+ At-3+ … + At-n) / n

Ft = forecast for the coming period

n = number of periods to be averaged

At-n = actual occurrences in the past period/past two periods/etc.

The main disadvantage in calculating a moving average is that all individual elements must be carried as data because a new forecast period involves adding new data and removing earlier data.

A weigthed moving average allows any weights to be placed on each element, provided that the sum of all weights equals 1. By forecasting this way with past data, more recent data is given more significance than older data. The formula is:

Ft= w1At-1+ w2At-2+ … + wnAt-n

wn = weight to be given to the actual occurrence for the period t-n

n = total number of prior periods in the forecast

The major drawback of using these two methods is the need to continually carry a large amount of historical data. Exponential smoothing uses weights for past data that decrease exponentially (1 – α) for each past period. This is the most used technique for forecasting. It is easily to use, the forecasts are surprisingly accurate and little computation is necessary to use the model. Three pieces of data are needed to forecast the future:

1. The most recent forecast;

2. The actual demand that occurred for the forecast period;

3. A smoothing constant alpha (α): the parameter in the exponential smoothing equation that controls the speed of reaction to differences between forecasts and actual demand. This is determined by both the nature of the product and the manager’s sense of what constitues a good response rate. The more rapid the growth, the higher the reaction rate should be.

Ft= Ft-1+ α (At-1 – Ft-1)

Ft = the exponentially smoothed forecast for period t

Ft-1 = the exponentially smoothed forecast made for the prior period

At-1 = the actual demand in the prior period

α = the desired response rate, or smoothing constant

This equation states that the new forecast is equal to the old forecast plus a portion of the error (the difference between the previous forecast and what actually occurred).

An upward or downward trend in data collected over a sequence of time periods causes the exponential forecast to always lag behind (be above or below) the actual occurrence. By adding another constant, the smoothing constant delta (δ), this trend can be corrected somewhat. Both alpha and delta reduce the impact of the error that occurs between the actual and the forecast. The formulas to compute the forecast including trend (FIT) are:

Ft= FITt-1+ α (At-1 – FITt-1)

Tt= Tt-1+ δ (Ft – FITt-1)

FITt= Ft+ Tt

Ft = the exponentially smoothed forecast that does not include trend for period t

Tt = the exponentially smoothed trend for period t

FITt(-1) = the forecast including trend for period t or the prior period

At-1 = the actual demand for the prior period

These equations need to be taken step for step to make an exponential forecast that includes trend. See example 3.1 for an example.

The smoothing constants need to be given a value between 0 and 1. Typically, values are used for alpha and delta in the range of .1 to .3. The values depend on how much random variation there is in demand and how steady the trend factor is.

Regression is a functional relationship between two or more correlated variables. It is used to predict one variable, given the other. The data should be plotted first to see if they appear linear. Linear regression refers to a special class of regression where the relationship between variables forms a straight line. Form of the formula: Y = a + bt, where Y is the value of the dependent variable that we are solving for, a is the Y intercept, b is the slope and t is an index for the time period. Linear regression is useful for long-term forecasting of major occurrences and aggregate planning. The biggest drawback of using linear regression forecasting is that past data and future projections are assumed to fall in about a straight line. However, it can still be used for both time series forecasting and causal relationship forecasting. See example 3.2 for an example.

A time series is chronologically ordered data that may contain one or more components of demand: trend, seasonal, cyclical, autocorrelation and random. Decomposition of a time series means identifying and separating the time series data into these components. The trend and seasonal component are relatively easy to identify, while cycles, autocorrelation and random components are much harder to identify.

There are two types of seasonal variation:

1. Additive seasonal variation: assumes that the seasonal amount is a constant, no matter what the trend or average amount is. See exhibit 3.9A for an example.

   Forecast including trend and seasonal = Trend + Seasonal

2. Multiplicative seasonal variation: the trend is multiplied by the seasonal factors. See exhibit 3.9B.

   Forecasting including trend and seasonal = Trend x Seasonal factor

A seasonal factor is the amount of correction needed in a time series to adjust for the season of the year. Example 3.3 and example 3.4 shows how seasonal indexes are determined and used to forecast.

Using least squares regression can also do decomposition. The process is as follow:

1. Decompose the time series into its components:

   1. Find seasonal component;

   2. Deseasonalize the demand;

   3. Find trend component.

2. Forecast future values of each component:

   1. Project trend component into the future;

   2. Multiply trend component by seasonal component.

Exhibit 3.11 shows an example of this process.

The forecast error is the difference between actual demand and what was forecast. These errors are called residuals. There are two factors that need to be discussed:

1. Sources of error: errors can come from a variety of sources. Errors can be classified as bias or random. Bias errors occur when a consistent mistake is made, for example by using the wrong variables. Random errors cannot be explained by the forecast model being used.

2. Measurement of error: there are terms to describe the degree of error: standard error, mean squared error (or variance) and mean absolute deviation. De mean absolute deviation (MAD) is the average of the absolute value of the actual forecast error. It measures the dispersion of some observed value from some expected value.

   MAD = () / n

t = period number
At = actual demand for the period t
Ft = forecast demand for the period t
n = total number of periods
| | = the absolute value

When the errors that occur in the forecast are normally distributed (the usual case), the MAD relates to the standard deviation as:

1 standard deviation = x MAD, or approximately 1.25 MAD

1 MAD = approximately 0.8 standard deviation

Another measure of error is the mean absolute percent error (MAPE). This measures the average error as a percentage of average demand.

MAPE = MAD / Average demand

A tracking signal (TS) is a measurement that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand. This is used to detect forecast bias. See also exhibit 3.14 and exhibit 3.15.

TS = RSFE / MAD

RSFE = the running sum of forecast errors, considering the nature of the error

MAD = the average of all the forecast errors, the average of the absolute deviations

Causal relationship forecasting involves using independent variables other than time to predict future demand. See example 3.5 for an example.

Multiple regression analysis is another forecasting method, in which a number of variables are considered, together with the effects of each on the item of interest.

S = B + Bm (M) + Bh(H) + Bi(I) + Bt(T)

S = gross sales for year

B = base sales, a starting point from which other factors have influence

M/H/I = other factors, such as marriages during the year/housing starts/etc.

T = time trend (first year = 1, second year =2, etc.)

Next to these quantitative methods of forecasting, there are also qualitative forecasting techniques. The knowledge of experts is important, while it requires much judgement. Examples are market research, panel consensus, historical analogy and the Delphi method. Collaborative Planning, Forecasting and Replenishment (CPFR) is a Web-based tool used to coordinate forecasting, production and purchasing in a firm’s supply chain. See exhibit 3.17. The five steps are:

1. Creation of a front-end partnership agreement;

2. Joint business planning;

3. Development of demand forecasts;

4. Sharing forecasts;

5. Inventory replenishment.

Chapter 4

Capacity Management in Operations is the ability to hold, receive, store or accommodate a number of customers in a system. Capacity is the amount of resource inputs available relative to output requirements over a particular period of time. However, it does not imply the duration of its sustainability. When looking at capacity, operations managers look at inputs and outputs. Operations management focus also the time dimension of capacity. Capacity planning is views in the following three time durations:

Long range: greater than one year;

Intermediate range: monthly or quarterly plans for the next 6 to 18 months;

Short range: less than one month.

Strategic capacity planning is finding the overall capacity level of capacity-intensive resources to best support the firm’s long-term strategy.

Capacity is the output that a system is capable of achieving over a period of time. The Best Operating Level is a level of capacity for which the process was designed and thus is the volume of output at which average unit cost is minimized. The determination of the minimum is difficult as it includes a complex trade-off between the allocation of fixed overhead costs and other costs. A measure to reveal how close a firm is to its best operation level is by calculating the capacity utilization rate:

Capacity utilization rate = Capacity used / Best operating level

When there are economies of scale, there is a cost advantage for companies as the volume increases because the average cost per unit of output drops. At some point, the size of a plant becomes too large and diseconomies of scale become a problem.

A focused factory means that a production facility works best when it focuses on a fairly limited set of production objectives. This concept is focused on the capacity by operationalizing the mechanism by plant within a plant (PWP). This is an area in a larger facility that is dedicated to a specific production objective. A focused factory may have several PWPs, as shown in exhibit 4.1.

Capacity flexibility is the ability to rapidly increase or decrease production levels, or to shift production capacity quickly from one to another. Flexibility can be created by:

Flexible plants: the ultimate in plant flexibility is the zero-changeover time plant. Such a plant can adapt to change by the use of movable equipment, knockdown walls, and easily accessible utilities.

Flexible processes: flexible manufacturing processes permit low-cost switching from one product to another and enable economies of scope: multiple products can be produced at lower cost in combination than they can be seperately.

Flexible workers: flexible workers have multiple skills and the ability to switch easily from one kind of task to another.

By changing the capacity, it is important to maintain system balance, keep track of frequency of capacity additions or reductions, and the use of external capacity (see exhibit 4.2).

In determining capacity requirements, the demands for individual product lines, plant capabilities and allocation of production need to be addressed. Typically this is done according to the following steps:

Use forecasting techniques to predict sales for individual products within each product line;

Calculate equipment and labor requirements to meet product line forecasts;

Project labor and equipment availabilities over the planning horizon.

Often the firm decides on capacity cushion: refers to the amount of capacity in excess of expected demand. It is the reserve capacity that handles sudden increases in demand or temporary losses of production capacity. See example 4.1.

To lie out the steps of a capacity problem, a decision tree can be used. A decision tree is a schematic model of the sequence of steps in a problem and the conditions and consequences of each step. Squares represent decision points, while circles represent chance events. To solve the decision tree, you need to work from the end of the tree backward to the start of the tree. See example 4.2.

Capacity planning in services knows several important differences with capacity planning in products:

Time: services cannot be stored for later use.

Location: the service setting must be at the place where the customer needs it.

Volatility of demand: the volatitility of demand for a service is much higher than that on a manufacturing product system. This is because of (1) services cannot be stored, so inventory cannot smooth the demand; (2) customers interact directly with the production system, and because they differ from each other, there will be more variability; (3) demand is directly affected by consumer behaviour, and this can be influenced by a lot of different things.

Planning capacity levels for services must consider the day-to-day relationship between service utilization and service quality. Exhibit 4.6 shows a service situation using waiting line terms. Arrival rate is the average number of customers that come to a facility during a specific period of time. Service rate is the average number of customers that can be processed over the same period of time when the facility is operating at maximum capacity.

Chapter 5

A project is a series of related jobs, usually directed toward some major output and requiring a significant period of time to perform. Projects can be categorized in four major areas: product change, process change, research and development, and alliance and partnership (see exhibit 5.1). Project management is planning, directing and controlling resources (people, equipment, material) to meet the technical, cost and time constraints of a project.

There are three types of projects:

• Pure projects: a structure for organizing a project where a self-contained team works full time on the project.

• Functional project: a structure where team members are assigned from the functional units of the organization. The team members remain a part of their functional units and typically are not dedicated to the project.

• Matrix project: a structure that blends the functional and pure project structures. Each project uses people from different functional areas. A dedicated project manager decides what tasks need to be performed and when, but the functional manager control which people to use.

A project starts out as a statement of work (SOW): a description of the objectives to be achieved, with a brief statement of the work to be done and a proposed schedule specifying the start and completion dates. A task is a further subdivision of the project. A subtask can be used if needed, to further subdivide the project into more meaningful pieces. A work package is a group of activities combined to be assignable to a single organizational unit. A project milestone is a specific event in a project. The work breakdown structure (WBS) defines the hierarchy of project tasks, subtasks and work packages. This structure is given in exhibit 5.2. Exhibit 5.3 shows an example of the WBS. Activities are pieces of work within a project that consume time. The completion of all the activities of a project marks the end of the project.

Exhibit 5.4 shows a sample of the available chart to control a project. A Gannt chart (also bar chart) shows in a graphic manner the amount of time involved and the sequence in which activities can be performed (see exhibit 5.4A).

Earned value management (EVM) is a technique that combines measures of scope, schedule and cost for evaluating project progress. Essential features of an EVM implementation are:

1. A project plan that identifies the activities to be accomplished;

2. A valuation of each activity work. If a project generates revenue, this is called the Planned Value (PV) of the activity. If a project generates costs, this is called the Budgeted Cost of Work Scheduled (BCWS);

3. Predefined “earning or costing rules” (also “metrics”) to quantify the accomplishment of work, called Earned Value (EV) or Budgeted Cost of Work Performed (BCWP).

There can be project tracking without EVM. This is the case when a project is evaluated based on cost. See exhibit 5.5A. The other charts in exhibit 5.5 shows project tracking with EVM. Example 5.1 shows another example.

A critical path of activities is the sequence(s) of activities in a project that forms the longest chain in terms of their time to complete. If one of the activities in the critical path is delayed, then the entire project is delayed. It is possible that there are multiple paths of the same length through the network, so there are multiple critical paths. The critical path method (CPM) can be used for scheduling a project. The following steps need to be taken:

1. Identify each activity to be done in the project and estimate how long it will take to complete each activity.

2. Determine the required sequence of activities and construct a network reflecting the precedence relationships. The easiest way to do this is by identifying immediate predecessors. These are activities that need to be completed immediately before another activity.

3. Determine the critical path.

4. Determine the early start/finish and late sart/finish schedule. For some activities, there can be slack time: the time that an activity can be delayed, without delaying the entire project. It is also the difference between the late and early start times of an activity.

See example 5.2.

An early start schedule is a project schedule that lists all activities by their early start times. A late start schedule is a project schedule that lists all activities by their late start times. This schedule may create saving by postponing purchases of material and other costs associated with the project. Exhibit 5.7 shows an example of the equations.

If a single estimate of the time required to complete an activity is not reliable, the best procedure is to use three time estimates. For an example of this method (including formulas!), see example 5.3.

Time-cost models are an extension of the critical path models that considers the trade-off between the time required to complete an activity and cost. This is often referred to as “crashing” the project. The basic assumption is that there is a relationship between activity completion time and the cost of a project. Crashing means the time to complete the project is compressed or shortened. Costs that are associated with expediting activities are activity direct costs and add to the project direct cost. Costs that are associated with sustaining the project are project indirect costs: overhead, facilities and resource opportunity costs. Between these two costs, there is a trade-off and therefore also a minimum point. The procedure for project crashing consists of the following five steps (see also exhibit 5.10):

1. Prepare a CPM-type network diagram. For each activity, this diagram should list:

   1. Normal cost (NC): the lowest expected activity costs;

   2. Normal time (NT): the time associated with each normal cost;

   3. Crash time (CT): the shortest possible activity time;

   4. Crash cost (CC): the cost associated with each crash time.

2. Determine the cost per unit of time (days) to expedite each activity.
   Slope = (CC – NC) / (NT – CT)

3. Compute the critical path.

4. Shorten the critical path at the least cost.

5. Plot project direct, indirect and total-cost curves and find the minimum-cost schedule (see exhibit 5.2).

In addition to scheduling each task, resources need to be assigned. Modern software can be used for this. To resolve overallocations, you can either add resources or reschedule.

Chapter 6

What is required to process something can be divided into three simple steps: (1) sourcing the parts we need, (2) making the item and (3) sending the item to the customer. Exhibit 6.1 illustrates these steps. Depending on the wishes of the customer, the lead time of products differs: the time needed to respond to a customer order. The different products can be classified in different groups, according to the

customer order decoupling point (CODP): determines where inventory is positioned to allow process or entities in the supply chain to operate independently. It separates order-driven activities from forecast-driven activities. As closer the decoupling point is to the customer, the quicker the customer can be served. The different groups are:

• Make-to-stock: firms that serve customers from finished goods inventory. Essential issue in satisfying customers is to balance the level of inventory against the level of customer service. Firms applying make-to-stock use lean manufacturing to achieve higher service levels for a given inventory investment

• Assemble-to-order: used by those firms that combine a number of preassembled modules to meet a customer’s specification.

• Make-to-order: used by firms that make the customer’s product from raw materials, parts and components.

• Engineer-to-order: used by firms working with the customer to design the product, and then make it from purchased materials, parts and components.

Essential issue in satisfying customers in a make-to-stock environment is to balance the level of inventory against the level of customer service.

• Easy with unlimited inventory but inventory costs money;

• Trade-off between the costs of inventory and level of customer service must be made;

• Forecasting is very important task.

Firms applying make-to-stock use lean manufacturing to achieve higher service levels for a given inventory investment Lean manufacturing: to achieve high customer service with minimum levels of inventory investment.

An assemble-to-order environment:

• Requires a design that enables as much flexibility as possible in combining components;

• Significant advantages from moving the customer order decoupling point from finished goods to components;

• Manufacturing results in customer specific products, assembled in a similar way. Total number of combinations that can be made can be calculate as follows:

N1x N2x … x Nn

Exhibit 6.2 shows an example of a make-to-stock Process Map. Material in a process is in one of these two states: (1) the material is moving or “in-transit” : “work-in-process inventory”; (2) the material is sitting in inventory and acting as a “buffer” waiting to be used. A common measure is the total average value of inventory: the total investment in inventory at the firm, which includes raw material, work-in-progress, and finished goods. The inventory turn is an efficiency measure where the cost of goods sold is divided by the total average value of the inventory. A measure directly related is days-of-supply, which is a measure of the number of days of supply of an item.

Simple systems can be analysed quickly by using Little’s law, which says there is a long-term relationship between the inventory, throughput, and flow time. Formula:

Inventory = Throughput rate x Flow time

Throughput rate is the average rate (units/days) that items flow through a process. Flow time is the time it takes one unit to completely flow through a process. Little’s law only works if the process is stabilized. See example 6.1.

Process selection refers to which kind of production process to use to produce a product or provide a service. There are different formats by which a facility can be arranged. The five basic structures are:

1. Project layout: product remains in a fixed location and manufacturing equipment is moved to the product rather than vice versa. Construction sites are an example.

2. Workcenter: similar equipment or functions are grouped together, such as all drilling machines in one area and all stamping machines in another. This process has much flexibility and can produce much different products, but mostly on a low-volume.

3. Manufacturing cell: dedicated area where products that are similar in processing requirements are produced. These cells are designed to perform a specific set of different cells in a production area, and the cells are dedicated to a limited range of products.

4. Assembly line: work processes are arranged according to the progressive steps by which the product is made. These steps are defined so that a specific production rate can be achieved. An example is automobile manufacturing.

5. Continuous process: similar to an assembly line in that production follows a predetermined sequence of steps, but the flow is continuous such as with liquids or drugs.

The relation between layout structures is often illustrated on a product-process matrix (see exhibit 6.3). The vertical axis shows the product standardization, the horizontal axis shows the volume of the products. The diagonal shows the different forms. From upper left to down right is shown the following: project, workcenter, manufacturing cell, assembly line and continuous process.

Exhibit 6.4 shows an example of a manufacturing cell.

The most common assembly line is a moving conveyor that passes a series of workstations in a uniform time interval called the workstation cycle time, which is the time between successive units coming off the end of an assembly line. The work performed at each station is made up of many bits of work, called tasks. The total work to be performed at a workstation is equal to the sum of the tasks assigned to that workstation. The assembly-line balancing problem is the problem of assigning tasks to a series of workstations so that the required cycle time is met and idle time is minimized. The precedence relationship complicates this, because there is a required order in which tasks must be performed in an assembly process. The steps in balancing an assembly line are:

1. Specify the sequential relationships among tasks by using a precedence diagram, which is similar to the project network diagram of Ch. 5;

2. Determine the required workstation cycle time (C):

3. Cycle time (C) = production time per day / required output per day (in units)

4. Determine the theoretical minimum number of workstations (Nt):

Theoretical minimum (Nt) = sum of task times (T) / cycle time (C)

1. Select a primary and secondary assignment rule for tasks;

2. Assign tasks to workstations until the sum of the task times is equal to the workstation cycle time or no other tasks are feasible because of time or sequence restrictions.

3. Evaluate the efficiency of the balance:

4. Efficiency = Sum of task times (T) / (actual number of workstations (Na) x workstation cycle time (C))

5. Rebalance if needed. See example 6.2!

If there are problems in lines/balancing, possibilities to accommodate tasks are:

• To split the task;

• To share the task;

• To use parallel workstations;

• To use a more skilled worker;

• To work overtime;

• To redesign.

Assembly line balancing frequently results in unequal workstation times. Flexible line layouts such as in exhibit 7.6 are a common way of dealing with this problem. The U-shaped line with work sharing at the bottom can also resolve the imbalance.

Chapter 7

Classifying services can be done according customer contact: the physical presence of the customer in the system. Creation of the service refers to the work process involved in providing the service itself. Service systems with a high degree of customer contact are more difficult to control and to rationalize than systems with a low degree of customer contact, because the influence of the customer. The extent of contact is the percentage of time the customer must be in the system relative to the total time it takes to perform the customer service
Configuring services can be done with the help of a service-system design matrix (see exhibit 7.1). There are six different alternatives. The top of the matrix shows the degree of contact. There are different degrees of customer/server contact:

Buffered core: physically separated from the customer;

Permeable system: penetrable by the customer (telephone, face-to-face contact);

Reactive system: both penetrable and reactive to the customer’s requirements.

The greater the amount of contact, the greater the sales opportunity is. The left side of the matrix shows this. The right side shows the impact on production efficiency as the customer exerts more influence on the operation. The ways a service can be delivered are: contact via e-mail, Internet and on-site technology, phone contact, face-to-face tight specs, face-to-face loose specs and face-to-face total customization.

Exhibit 7.2 shows an extension of the design matrix. It shows the changes in workers, operations and types of technical innovations as the degree of customer/service system contact changes.

Service Blueprint is a standard flowchart tool for service process design. It emphases what is visible and what is not visible to the customer. Exhibit 7.3 shows an example. Blueprinting does not provide any direct guidance for how to make the process conform to that design. Therefore you can use poka-yokes: procedures that prevent mistakes from becoming defects.

A main problem in service settings is the management of waiting lines. The manager has to measure out the cost of waiting against the added cost of providing more service. Managing queues, the following suggestions are made:

Segment the customers;

Train your servers to be friendly;

Inform your customers of what to expect;

Try to divert the customer’s attention when waiting;

Encourage customers to come during slack periods.

The Queuing System consists of three major components:

Customer arrivals

Finite population: limited-size customer pool that will use the service and at times form a line.

Infinite population: large enough in relation to the service system so that the population size caused by subtractions or additions to the population does not significantly affect the system probabilities.

Distribution of arrivals

Arrival rate: the expected number of customers that arrive each period. A constant arrival distribution is periodic, while variable (random) arrival distributions is much more common. There are two ways to look at service arrivals: (1) analyse the time between successive arrivals to see if the times follow some statistical distribution. Usually, this time is exponentially distributed. (2) Choose a time length and try to determine how many arrivals might enter the system within this time (T). Usually, this time is Poisson distributed.

Exponential distribution: a probability distribution associated with the time between arrivals. See exhibit 7.6:

F(t)= λ e-λt, where λ is the mean number of arrivals per time period.

Poisson distribution: probability distribution for the number of arrivals during each time period (see exhibit 7.7). This is obtained by finding the probability of exactly n arrivals during T. If the arrival process is random, the distribution is the Poisson and the formula is:

PT(n) = ((λT)ne-λT)/ n!

The exponential and Poisson distributions can be derived from one another. The mean and variance of the Poisson are equal and denoted by λ. The mean of the exponential is 1/ λ and its variance is 1/ λ2.

Arrival patterns: another characteristic. The arrivals at a system are far more controllable than is generally recognized.

Size of arrival units: a single arrival is one unit. A batch arrival is some multiple of the unit.

Degree of patience: a patient arrival is one who waits as long as necessary. An impatient arrival is one who decides to leave after seeing the length of the line (balking) or joins the line but departs after a while (reneging).

The Queuing System: Factors that are not discussed yet:

Length: a waiting line can be infinite or finite, caused by legal restrictions or physical space characteristics. The length influences the arrival distribution.

Number of lines: there can be a single line or multiple lines.

Queue discipline: rules for determining the order of service to customers in a waiting line (First come, first served (FCFS)).

Service rate: capacity of the server in number of units per time period. If this is random, μ refers to the average number of units/customers that can be serviced per time period.

Line structure: as exhibit 7.9 shows, there are different line structures. The choice depends on the volume of customers served and on the restrictions imposed by sequential requirements governing the order in which service must be performed. The opportunities:

Single channel, single phase: simplest type of waiting line, can be found in a one-person barbershop.

Single channel, multiphase: series of services; e.g. carwash.

Multichannel, single phase: teller’s window in a bank. The difficulty with this format is the uneven service time given each customer results in unequal speed/flow among the lines.

Multichannel, multiphase: same to the preceding one, except that two or more services are performed in sequence. Example: admission of patients in a hospital. Because several servers are available for this procedure, more than one patient at a time may be processed.

Mixed: multi-to-single channel and alternative path structures.

Once a customer is served, two exit fates are possible:

The customer returns to the source population and immediately become a competing candidate for service again;

Low probability of reservice.

Practical: How to solve exam questions

Identify the appropriate waiting line model;

Determine: h and m;

Identify the appropriate performance measure;

Find the correct formula;

Fill out the formula.

To identify the appropriate waiting line model, there are two options given: M/M/1 (refers the arrival process) and M/D/1(refers to the service process). Exhibit 7.10 discusses three waiting line models: (1) simple system, (2) constant service time system and (3) multichannel system. The equations are given in exhibit 7.11:

Model 1:

Lq= λ2/ µ (µ - λ)
Ls= λ / µ - λ

Wq= Lq/ λ

Ws= Ls/ λ
Pn= (1 - ) ()2

P0= (1 - )

ρ =

Model 2:

Lq= λ2/ 2µ (µ - λ)
Ls= Lq+

Wq= Lq/ λ

Ws= Ls/ λ

Model 3:

Ls= Lq+

Wq= Lq/ λ

Ws= Ls/ λ

Pw= Lq( – 1)

λ = Mean number of arrivals per time period.

m = Mean number of people (or items) served per time period.

Average queue time, Wq

Average queue length, Lq

Average time in system, Ws

Average number in system, Ls

Probability of idle service facility, P0

Utilization, r

Sometimes, using a simple formula cannot solve waiting line problems. Therefore, computer simulation can be used.

Chapter 8

Sales and operations planning is a process that helps firms to better manage demand of the customer. An aggregate operations plan can be prepared for this: a plan for labour and production for the intermediate term, with the objective to minimize the cost of resources needed to meet demand. Aggregate is the management of major groups of products.

Exhibit 8.1 shows an example of the sales and operations planning relative to other major operations planning activities. Sales and operations planning (S&OP) is the process that companies use to keep demand and supply in balance and to coordinate distribution, marketing and financial plans. Product families do aggregation on the supply side, and groups of customers do this on the demand side. The time dimension in exhibit 8.1 is shown as long, intermediate and short range. Long-range planning is generally done annually, and focuses on a horizon greater than one year. Intermediate-range planning covers mostly a period from 3 to 18 months. Short-range planning focuses from one day to six months.

The main purpose of an aggregate plan is to specify the optimal combination of production rate, workforce level and inventory on hand. Production rate is the number of units completed per unit of time. Workforce level refers to the number of workers needed for production (production = production rate x workforce level). Inventory on hand is unused inventory carried over from the previous period.

Exhibit 8.2 shows the internal and external factors that constitute the production-planning environment. In general, the external environment is not manageable. However, there are companies in which demand for the product can be managed. If there are cyclical demand fluctuations, demand can be smoothed by using complimentary products. To manage internal factors, accurate response is used: the refined measurement of historical demand patterns blended with expert judgement to determine when to begin production of particular items.

Production planning strategies are plans for meeting demand that involve trade-offs in the number of workers employed, work hours, inventory and shortages. These strategies can be helpful in managing demand. There are three different strategies:

1. Chase strategy: match the production rate to the order rate by hiring and laying off employees as the order rate varies. The success of this strategy depends on having a pool of easily trained applicants to draw on as order volumes increase. This strategy influences the motivation of employees.

2. Stable workforce – variable work hours: vary the output by varying the number of hours worked through flexible work schedules or overtime. This strategy provides workforce continuity and avoids the motivational impacts on employees as with a chase strategy.

3. Level strategy: work with a stable workforce and a constant output rate. Shortages and surpluses are absorbed by fluctuating inventory levels, order backlogs and lost sales. Employees benefit from a stable environment, but there can be potentially decreased customer service levels and increased inventory costs.

If one of these strategies is used to manage demand, a pure strategy is used. If more than one strategy is used, a mixed strategy is used.

There are four costs relevant to the aggregate production plan:

1. Basic production costs: the fixed and variable costs of producing a product; direct and indirect labour costs and compensation are included.

2. Costs associated with changes in the production rate: typical costs are hiring, training and laying off personnel. Hiring temporary help is a way of avoiding these costs.

3. Inventory holding costs: the cost of inventory, storing, insurance, taxes, spoilage and obsolence.

4. Backordering costs: very hard to measure. Include costs of expediting, loss of customer goodwill and loss of sales revenue resulting from backordering.

The aggregate production plan is the key for the success of budgeting. Accurate medium-range planning increases the likelihood of (1) receiving the requested budget and (2) operating within the limits of the budget.

Companies often use a cut-and-try method to develop an aggregate plan. This involves drafting alternatives and choosing the best one. Carefully look at the following: exhibit 8.3, exhibit 8.4, exhibit 8.5 and exhibit 8.6. Also look at exhibit 8.7 and exhibit 8.8.

Yield management is the process of allocating the right type of capacity to the right type of customer at the right price and time to maximize revenue or yield, given that capacity is limited. Yield management can be used to making demand more predictable. From an operational perspective, yield management is most effective when:

1. Demand can be segmented by the customer;

2. Fixed costs are high and variable costs are low;

3. Inventory is perishable;

4. Product can be sold in advance;

5. Demand is highly variable.

Yield management only works if pricing structures appear logic to the customer and justify the different prices. Such justification, also called rate fences, may have either a physical basis (a room with a view) or a nonphysical basis (unrestricted access to the Internet). Pricing should also depend on capacity.

A second issue in yield management is handling variability in arrival or starting times, durations and time between customers. This entails using forecasting methods. A third issue relates to managing the service process. A last and most critical issue is training workers and managers to work in an environment where overbooking and price changes are standard occurrences that directly impact the customer.

The essence of yield management is the ability to manage demand. Kimes and Chase suggest two strategic levers that can be used to accomplish this goal: pricing and duration control. If these are thought of in matrix form (see exhibit 8.9) with fixed or variable price and predictable or unpredictable duration, a framework arises for a firm to identify its position and the necessary actions to manage yield.

Chapter 9

Enterprise resource planning (ERP) is a computer system that integrates application programs in accounting, sales, manufacturing and the other functions in a firm. Different application programs share 1 database, which can significantly benefit a firm. Exhibit 9.1 shows typical operations and supply chain function in a diagram.

The emphasis is on material requirements planning (MRP), which is the logic for determining the number of parts, components and materials needed to produce a product. The system is based on dependent demand. MRP is most valuable in industries where a number of products are made in batches using the same productive equipment. Exhibit 9.2 states a list of examples in different industries and the expected benefit from MRP.

A master production schedule (MPS) is a time-phased plan specifying how many and when the firm plans to build each end item. MPS states when the end items need to be finished. This can be used as an input to the MRP process, except if the end item is quite large or expensive. Making a MPS is dependent on the pressures from various functional areas and deadlines that are set. To ensure a good MPS, the master scheduler (the person) must:

• Include all demands from product sales, warehouse replenishment, spares and interplant requirements;

• Never lose sight of the aggregate plan;

• Be involved with customer order promising;

• Be visible to all levels of management;

• Objectively trade off manufacturing, marketing and engineering conflicts;

• Identify and communicate all problems.

The upper part of exhibit 9.3 shows the aggregate plan for the total number of mattresses planned per month, the lower part shows a MPS, which gives far more details. So, firstly an aggregate operations plan need to be made, then a MPS and finally the MRP program can be used.

Flexibility of a MPS depends on the production lead-time, commitment of parts and components to a specific end item, the relationship between the customer and vendor, the amount of excess capacity and the reluctance or willingness of management to make changes. The goal of time fences is to maintain a reasonably controlled flow through the production system. A time fence is a period of time having some specified level of opportunity for the customer to make changes. See exhibit 9.4 for an example. Frozen means that absolutely no changes/only minor changes can be made. Slushy refers to changes in specific products within a product group as long as parts are available. Liquid may allow almost any variations in production, with the provision that capacity remains the same and there are no long lead-time items involved. Available to promise is a feature of MRP systems that identifies the difference between the numbers of units currently included in the master schedule and the actual (firm) customer orders.

Exhibit 9.5 shows an overview of the input to a standard MRP program and the reports generated by the program. Firstly, the MPS states the number of items to be produced during specific time periods. A bill of materials (BOM) identifies the specific materials used to make each item and the correct quantities of each. The inventory records file states the number of units on hand and on order. These three sources are input for the material requirements program.

The demand for products comes primarily from two main sources: (1) from customers who have placed specific orders and have a promised delivery date; (2) from the aggregate production plan, which reflects the firm’s strategy for meeting demand in the future.

The BOM file contains the complete product description and lists the materials, parts and components, and also the sequence in which the product is created. It is also called the product structure file or product tree, because it shows how a product is put together. See exhibit 9.6. To simplify the purchasing process, all identical items need to be placed at the same level. See exhibit 9.7.

In exhibit 9.8 you can find the variety of information contained in inventory records. The MRP program accesses the status segment of the record according to specific time periods (time buckets).

A MRP table is given in the lecture slides. The following definitions are given in this and mean:

• Gross requirements: customer demand in this period;

• Scheduled receipts: orders in production at this moment;

• Projected available: amount of inventory expected at the end of the period;

• Net requirements: the absolut value of the provisional stock;

• Planned order receipts;

• Planned order releases;

• Q = batch quantities;

• LT = lead time;

• OH = on hand inventory;

• SS = safety stock

• Provisional Projected available (t) = Projected available (t – 1) – Gross requirements (t) + Scheduled receipts (t);

• For the first period, apply OH – SS in stead of Projected available (t – 1).

Projected available (t) = Projected available (t – 1) – Gross Requirements (t) + Scheduled receipts (t) + Planned order receipts (t)

To realise a planned order receipt in period t, the order need to be released in period t – LT.

• Planned order past due (PODP): an order receipt is planned in week t, but cannot be released in this week: t – LT is before the first week in the table.

• Rescheduled in: a scheduled receipt is too late: in a previous period there is already a net requirement which leads to a PODP. There always needs to be a POPD for a Reschedule In, but not always a Reschedule In after each PODP: only if there is an Scheduled Receipt after a PODP.

• Rescheduled out: a Scheduled Receipt in period t is unnecessarily early. In this period is Projected Available ≥ Q (batch size).

Solutions for a POPD:

1. Use Safety Stock (SS): discuss with sales;

2. Accelerate (a part of) the Scheduled Receipt: discuss with production;

3. Rush order in stead of current Planned Order;

4. Adjust Gross Requirements: at final product level you need to discuss this with sales/customers, at component level you need to look at the impact on the BOM higher levels, because the need is due to release of higher level. This is called order pegging.

For an example about the use of MRP, look at exhibit 9.9, exhibit 9.10, exhibit 9.11, exhibit 9.12 and exhibit 9.13. See also example 9.1.

The determination of lot sizes in an MRP system is complex and difficult. Lot sizes are the part quantities issued in the planned order receipt and planned order release sections of an MRP schedule. There are different techniques. The different forms are:

1. Lot-for-lot is the most common used technique. It sets planned orders to exactly match the net requirements, it produces exactly what is needed each week without carrying over into future periods, it minimizes carrying cost and it does not take into account setup costs or capacity limitations. See exhibit 9.14.

2. Economic Order Quantity is discussed in Ch. 11. In an EOQ model there is either fairly constant demand or safety stock must be kept to provide for demand variability. The EOQ model uses an estimate of total annual demand, the setup/order cost and the annual holding cost. It assumes that parts are used continuously during the period and there generates lot sizes that do not always cover the entire number of periods. Exhibit 9.15 gives an example.

3. Least Total Cost is a dynamic lot-sizing technique that calculates the order quantity by comparing the carrying cost and the setup/ordering costs for various lot sizes, and then selects the lot size in which these costs are almost the same. Exhibit 9.16 gives examples.

4. Least Unit Cost is a dynamic lot-sizing technique that adds ordering and inventory carrying cost for each trial lot size and divides by the number of units in each lot size, and picks then the lot size with the lowest unit costs. See exhibit 9.17.

Chapter 10

Total Quality Management (TQM) may be defined as ‘managing the entire organization so that it excels on all dimensions of products and services that are important to the customer’. It has two fundamental operational goals:

Careful design of the product or service;

Ensuring that the organization’s systems can consistently produce the design.

The Malcolm Baldrige National Quality Award is an award established by the U.S. Department of Commerce and given annually to companies that excel in quality. Exhibit 10.1 (p. 307) shows different definitions of what quality is and how to achieve it.

Fundamental to any quality program is the determination of quality specifications and the costs of achieving/not achieving those specifications. Design quality refers to the inherent value of the product in the marketplace in thus a strategic decision for the firm. The dimensions of design quality are stated in exhibit 10.2 (p. 308):

Performance Primary product or service characteristics

Features Added touches, bells and whistles, second characteristics

Reliability/Durability Consistency of performance over time

Serviceability Ease of repair

Aesthetics Sensory characteristics (sounds, feel, look..)

Perceived quality Past performance and reputation

Conformance quality refers to the degree to which the product or service design specifications are met. It involves activities essential in achieving conformance. Quality at the source is concerned with a person’s work responsibility for making sure that the output corresponds the specification. Exhibit 10.3 (p. 310) shows examples of the dimensions of quality: the criteria by which quality is measured.

Cost of Quality (COQ) analysis is one of the primary functions of the QC departments. These costs are expenditures related to achieving product or service quality. The COQ can be classified into four types:

Appraisal costs Inspection, testing

Prevention costs Finding quality problems, training

Internal failure costs Scrap, rework, repair in a company

External failure costs Repair, loss of goodwill, warranty replacement

Exhibit 10.4 (p. 312) shows an example of how quality costs are reported.

ISO 9000 and ISO 14000 involve a series of standards agreed upon by the International Organization for Standardization (ISO). This approach was adopted in 1987 in more than 160 countries.

Six Sigma refers to the method companies use to eliminate defects in their products and processes. It seeks to reduce variation in the processes that lead to product defects. The Six-Sigma thinking allows managers to describe performance of a process in terms of its variability and to compare it using the defects per millions opportunity (DPMO) metric. Defects per million opportunities (DPMO) requires three pieces of data (see example 10.1, p. 314):

Unit The item produced or being serviced

Defect Any item/event that does not meet the customers’ requirements

Opportunity A chance for a defect to occur

DPMO = Number of defects___________________________* 1,000,000

Number of opportunities for error per unit x Number of units

The methodology side of Six-Sigma are project-oriented through the Define, Measure, Analyse, Improve, and Control (DMAIC) cycle. It is used to set the focus on the understanding and achieving what the customer wants. By the integration of analytical tools for Six-sigma, DMAIC categories can be illustrated. In exhibit 10.5 on page 316-317 the analytical Tools for Six Sigma and Continuous Improvement are depicted. These tools are (1) flowcharts, (2) run charts, (3) Pareto charts, (4) checksheets, (5) cause-and-effect diagrams, (6) opportunity flow diagram and (7) process control charts. Other tools are failure mode and effect analyse (see exhibit 10.6, p. 318) and design of experiments (DOE).

Statistical quality control (SQC) covers the quantitative aspects of quality management. Processes that provide goods and services usually exhibit some variation in their output. Assignable variation is the deviation in the output of a process that can be clearly identified and managed, e.g. workers are not trained. Common variation is the deviation in the output of a process that is random and inherent in the process itself, for example caused by the type of equipment used to complete a process. Statistical values involved by this are the mean and standard deviation.

Mostly, when variation is reduced, quality is improved. However, it is impossible to have zero variability. Therefore there are upper and lower specification limits: the range of values in a measure associated with a process that is allowable given the intended use of the product or service. See exhibit 10.7 (p. 320). However, Taguchi (Japanese) has pointed at that the traditional view illustrated in this exhibit is nonsense, and he suggest that a more correct picture of the loss is shown in exhibit 10.8 (p. 321). Taguchi argues that being within specification is not a yes/no decision, but rather a continuous function.

Motorola made process capability and product design famous by adopting Six Sigma limits. The consistency of a process can be measured by the standard deviation. If the process deviates more than three standard deviations, the process is stopped. Process capability is the ability of a process to produce output within specification limits. This concept only holds meaning for processes that are in state of statistical control. See exhibit 10.9 (p. 323) and exhibit 10.10 (p. 324). The capability index is used to measure how well the process is capable of producing relative to the design specifications. The capability index (Cpk) is the ratio of the range of values produced by a process, divided by the range of values allowed by the design specification. If looking at exhibit 10.9 and 10.10, the capability index is the position of the mean and tails of the process relative to design specifications. The more off-centre, the greater the chance to produce defective parts.

Sometimes it is useful to calculate the actual probability of producing a defect. Firstly, the Z score associated with the upper and lower specification limits needs to be calculated.

See also example 10.2 on page 325.

Process control is concerned with monitoring quality while the product or service is being produced. Statistical process control (SPC) involves testing a random sample of output from a process to determine whether the process in producing items within a preselected range. Attributes are quality characteristics that are classified as either confirming or not conforming to specification. Exhibit 10.11 (p. 327) shows examples of how control charts can be analysed to understand how a process is operating.

A p-chart can be used to decide whether the item is good or bad. The upper and lower control limits need to be defined and be drawn on a graph. After that, the fraction defective of each individual sample tested need to be plotted.

UCL = p bar + zsp

LCL = p bar - zsp or 0 if less than 0

P bar is the fraction defective; sp is the standard deviation, n the sample size and z the number of standard deviations for a specific confidence. See example 10.3 (p. 328).

In the case of the p-chart, the item was either good or bad. There are times when the product/service can have more than one defect. A c-chart can be used to monitor the number of defects per unit. The c-chart is the Poisson. If c is the number of defects for a particular unit, than c bar is the average number of defects per unit, and the standard deviation is the square root of c bar.

See example 10.4 on p. 329.

X bar and R-charts are widely used in statistical process control. No attribute sampling is used, but variables sampling: the actual weight/volume/number of inches or other variable measurements are measured. Based on these measurements, control charts are developed to determine the acceptability or rejection of this process. There are four important issues to address in creating a control chart: (1) the size of the samples, (2) the number of samples, (3) the frequency of samples and (4) the control limits. If the standard deviation of the process distribution is known, the X bar-chart may be defined:

UCLX bar = X two bars + zSX bar and LCL = X two bars - zSX bar

An X bar-chart is a plot of the means of the samples that were taken from the process. X two bars is the average of the means.

In practice, the standard deviation of the process is not known. Therefore, an R-chart is often used, which is a plot of the average of the range within each sample. The range is the difference between the highest and lowest numbers in that sample.

Where X two bars is the average of the means of the samples, j the sample number, m the total number of samples, Rj the difference between the highest and lowest measurement in that sample and R bar the average of the measurement differences R for all samples. Exhibit 10.3 shows a table that allows you to easily compute the upper and lower control limits for both the X bar-chart and the R-chart. See also example 10.5 (p. 333).

Acceptance sampling is performed on goods that already exist to determine what percentage of products conforms to specifications. See example 10.6 (p. 335), the text afterwards, and example 10.7 on p. 336. Also take a look at exhibit 10.16 (p. 336) and exhibit 10.17 (p. 337).

Chapter 11

You should try to get down inventory as far as possible. Exhibit 11.1 shows different types of supply chain inventories that would exist in a make-to-stock environment. There are three inventory models. The techniques described here are most appropriate when demand is difficult to predict with great precision:

• The single-period model: used when a one-time purchase of an item is made, for example a T-shirt for a one-time sporting event.

• Fixed-order quantity model: used when we want to maintain an item in-stock, and when the item is resupplied, a certain number of units must be ordered each time. Inventory is monitored until it gets down to a level where the risk of stocking out is great enough that we are compelled to order.

• Fixed-time period model: also used when the item should be in-stock and ready to use, but the difference is that the item is ordered at certain intervals of time, for example each Thursday morning.

Inventory is the stock of any item or resource used in an organization. An inventory system is a set of policies and controls that monitor levels of inventory and determine what levels should be maintained, when stock should be replenished and how large orders should be. Manufacturing inventory are items that contribute to or become part of a firm’s product output. Inventory refers to the tangible goods to be sold and the supplies necessary to administer the service.

Purposes of having a supply of inventory can be:

• To maintain independence of operations;

• To meet variation in product demand;

• To allow flexibility in production scheduling;

• To provide a safeguard for variation in raw material delivery time;

• To take advantage of economic purchase order size;

• Many other domain-specific reasons.

In making decisions that affects inventory size, the following costs must be considered:

• Holding or carrying costs: all costs for holding goods and the storage facilities, taxes, insurance etc.

• Setup (production change) costs: the costs of making each different product.

• Ordering costs: the managerial and clerical costs to prepare the purchase or production order.

• Shortage costs: the costs of having a stock out.

Independent demand: the demands for various items are unrelated to each other. Dependent demand: the need for any one item is a direct result of the need for some other item, usually an item of which it is a part. Exhibit 11.2 is a framework that describes how demand, transaction cost and the risk of obsolete inventory map into different types of systems. Transaction cost is dependent on the level of integration and automation iincorporated in the system. Manual systems use a two-bin logic, which are dependent on human posting of the transactions to replenish inventory, which is relatively expensive compared to using a computer to automatically detect when an item needs to be ordered.

An inventory system provides the organizational structure and the operating policies for maintaining and controlling goods to be stocked. In a single-period inventory model, a decision is just a one-time purchasing decision where the purchase is designed to cover a fixed period of time and the item will not be reordered. An example is the “newsperson” problem. The optimal stocking level occurs at the point where the expected benefits derived from carrying the next unit are less than the expected costs for that unit. Co are the cost per unit of demand overestimated, Cu are the cost per unit of demand underestimated. The expected marginal cost equation becomes:

P(Co) ≤ (1 – P)Cu

Where P is the probability that the unit will not be sold and 1 – P the probability of it being sold. P is:

P ≤ Cu / (Co + Cu)

See also example 11.1.

Multiperiod inventory systems involves items that will be purchased periodically where inventory should be kept in stock to be used on demand. There are two types of systems. The fixed-order quantity model (EOQ/Q-model) is an inventory control model where the amount requisitioned is fixed and the actual ordering is triggered by inventory dropping to a specified level of inventory. The fixed-time period model (P-model) is an inventory control model that specifies inventory is ordered at the end of a predetermined time period. The interval of time between orders is fixed and the order quantity varies. Exhibit 11.3 shows the differences:

• The fixed-time period model has a larger average inventory because it must also protect against stock out during the review period T.

• The Q-model favors more expensive items because average inventory is lower.

• The Q-model is more appropriate for important items such as critical repair parts, because there is closer monitoring and therefore quicker response to potential stock out.

• The Q-model requires more time to maintain because every addition or withdrawal is logged.

Exhibit 11.4 shows what occurs when each of the two models is put into use and becomes an operating system.

The fixed-order quantity model tries to determine the specific point R at which an order will be placed and the size of that order, Q. Order point R is always a specified number of units. The inventory position is the amount on-hand plus on-order minus backordered quantities. In the case where inventory has been allocated for special purposes, the inventory position is reduced by these allocated amounts. Exhibit 11.5 is based on some unrealistic assumptions. The costs are:

Total annual cost = annual purchase cost + annual ordering cost + annual holding cost

TC = DC + S + H

TC = total annual cost

D = annual demand

C = cost per unit

Q = quantity to be ordered

S = setup cost

R = reorder point

L = lead time

H = annual holding and storage cost per unit of average inventory

The relations between the costs are graphed in exhibit 11.6.

The point where the total cost is a minimum, can be calculated as follows:

= 0 + ( + = 0

Qopt =

Because this simple model assumes constant demand and lead time, neither safety stock nor stock-out is necessary, and the reorder point R is simply: R = d (bar) L, where d bar is the average daily demand and L is the lead time in days. See example 11.2.

If there is variety in demand, there needs to be safety stock (SS): the amount of inventory carried in addition to the expected demand. In exhibit 11.7 is shown that demand after the reorder point can vary. If demand varies, the reorder point is:

R = d (bar) L + zσL

R = reorder point in units

d bar = average daily demand

L = lead time in days

z = number of standard deviations for a specified service probability

σL = standard deviation of usage during lead time

Demand can be determined by:

d = i

n

If the standard deviation needs to be computed over several days, it is:

σL =

Safety stock (SS) = zσL

See example 11.3 and example 11.4.

In a fixed-time period system, reorders need to be placed on time T and the SS that needs to be reordered is:

Safety stock = zσT+L

Exhibit 11.8 shows a graph. The quantity to order, q, is:

Order quantity = average demand over the vulnerable period + SS – Inventory currently on hand (plus on order, if any)

q = d bar (T+L) + zσT+L – I

See example 11.5.

Inventory turn = costs of goods sold/average inventory value.

How inventory is managed directly related to the financial performance of the firm. The average inventory and the inventory turn for an individual item is:

Average inventory value = (Q/2 + SS)C

Inventory turn = DC / ((Q/2 + SS)C) = D / (Q/2 + SS)

See example 11.6.

Price-break models are used for products where the selling price of an item varies with the order size. In example 11.8 and exhibit 11.9 this is collaborated further.

ABC inventory classification divides inventory into dollar volume categories that map into strategies appropriate for the category. A means high dollar volume, B moderate dollar volume and C low dollar volume. By using this classification, not every inventory needs to go through counting; the focus can be on the most important items in stock. This is related to the Pareto principle: the few having the greatest importance. Exhibit 11.11 shows a calculation.

Cycle counting is a physical inventory-taking technique in which inventory is counted on a frequent basis rather than once or twice a year.

Chapter 12

Lean production

Lean production: the integrated activities designed to achieve high-volume, high-quality production using minimal inventories of raw materials, work-in-process, and finished goods. Customer value is, in the context of lean production, something for which the customer is willing to pay. Waste is anything that doesn’t add value from the customer’s perspective. There are seven types of waste: production of defect products, waste of overproduction, inventory waste, waste of waiting time, unnecessary processing (repairs), waste of motion and transportation waste. Services operate in a sea of uncertainty and variability that are much harder to control, because of uncertainty in task times, uncertainty in demand and customers’ production roles. Lean production and Six Sigma work best in repeatable, standardized operations, but can be applied to services as well. Exhibit 12.1 shows a lean process.

The focus of the Toyota Production System is on elimination of waste and respect for people. Value stream: these are the value-adding and non-value-adding activities required to design, order, and provide a product from concept to launch, order to delivery, and raw materials to customers. Waste reduction relates to the optimization of value-adding activities and elimination of non-value-adding activities that are part of the value stream. These are different components of a supply chain that should use a lean focus:

Lean suppliers;

Lean procurement;

Lean manufacturing;

Lean warehousing;

Lean logistics;

Lean customers.

⇧

Value stream mapping (VSM)

Value stream mapping (VSM) is a graphical way to analyze where value is or not being added as material flows through a process. Exhibit 12.2 provides a sample map that depicts a production process. Exhibit 12.3 shows the value stream mapping symbols. Value stream mapping is a two-part process: first depicting the “current state” of the process and second a possible “future state”. Exhibit 12.4 depicts another map of the same process with suggested improvements. Kaizen is the Japanese philosophy that focuses on continuous improvement. The Kaizen bursts identify specific short-term projects that teams work on to implement changes in the process.

There is a set of key principles that can guide the design of lean supply chains. The first and second one relate to internal production processes, the third applies lean concepts to the entire supply chain. The principles include:

Lean Layouts:

Group technology

Quality at the source

JIT production

Lean Production Schedules:

Uniform plant loading

Kanban production control system

Determination of number of Kanbans needed

Minimized setup times

Lean Supply Chains:

Specialized plants

Collaboration with suppliers

Building a lean supply chain

⇧

Preventive maintenance

Preventive maintenance is emphasized to ensure that flows are not interrupted by downtime or malfunctioning equipment. This involves periodic inspection and repair designed to keep equipment reliable. Lean concepts:

Group Technology (GT) is a philosophy in which similar parts are grouped into families, and the processes required to make the parts are arranged in a manufacturing cell. See also exhibit 12.5.

Quality at the Source means do it right the first time and, when something goes wrong, stop the process or assembly line immediately.

Just-in-time (JIT) is a philosophy of continuous and forced problem solving that drives out waste (storage, inspection, waiting etc.). It is typically applied to repetitive manufacturing. JIT exposes problems that are otherwise hidden by inventory: see exhibit 12.6.

Lean production requires a stable schedule over a lengthy time horizon. Therefore, there are several lean production schedules:

Level schedule: a schedule that pulls material into final assembly at a constant rate and allows the various elements of production to respond to pull signals.

Freeze window: the period of time during which the schedule is fixed and no further changes are possible.

Backflush: calculating how many of each part were used in production and
using these calculations to adjust actual on-hand inventory balances. This eliminates the need to actually track each part used in production.

Uniform plant loading: smoothing the production flow to dampen schedule variation. When a change is made in a final assembly, the changes are magnified throughout the line and the supply chain. An example is shown in exhibit 12.7.

Kanban production control systems: Kanban is the Japanese translation for sign or instruction card. In those production control systems, only cards or containers are used to make up the Kanban pull system and regulate JIT flows. Level scheduling requires material to be pulled into final assembly in a pattern, which is uniform enough to allow the various elements production to respond to pull signals. Kaban significantly reduces the setup costs and changeover times achieving a smooth flow. See exhibit 12.8 and exhibit 12.9. The determination of the number of Kanbans needed can be done according to:

k = (Expected demand during lead time + SS) / size of the container

= (DL(1+S))/C

where k = number of kanban card sets, D = average number of units demanded per period, L = lead time to replenish an order, S = safety stcok expressed as a percentage of demand during the lead time and C = container size. A Kanban system does not produce zero inventory, but it controls the amount of material than can be in process at a time. See example 12.1.

Minimized Setup Times: reductions in setup and changeover times are necessary to achieve a smooth flow. Exhibit 12.10 shows the relationship between lot size and setup costs.

The following concepts are related to lean network design:

Specialized plants: small specialized plants rather than large vertically integrated manufacturing facilities are important. Speed and quick response to changes are keys to the success of a lean supply chain.

Collaboration with suppliers: if a firm shares its projected usage requirements with its vendors, vendors have a long-run picture of the demands and the firm can further reduce their buffer inventories. Maintaining stock at a lean level requires frequent deliveries during the day.

Building a lean supply chain: a supply chain is the sum total of organizations involved. To be lean, everyone’s got to be on the same page! Muda = waste.

10 of the more successful techniques applied to service companies to become more lean are:

Organize problem-solving groups;

Upgrade houskeeping;

Upgrade quality;

Clarify process flows;

Revise equipment and process technologies;

Level the facility load;

Eliminate unneccessary activities;

Reorganize physical configuration;

Introduce demand-pull scheduling;

Develop supplier networks.

Practice Questions Chapter 12: Lean production

What is lean production?

What is a value stream and what is waste reduction?

What do the terms lean suppliers, procurement, manufacturing systems, warehousing, logistics and customers mean?

What do the terms group technology, quality at the source and JIT production mean?

How are stable schedules accomplished?

Which concepts can contribute to a lean supply chain?

What are value stream mapping and Kaizen?

Chapter 13

Procurement and Global Sourcing

Strategic sourcing is the development and management of supplier relationships to acquire goods and services in a way that aids in achieving the immediate need of the business. Exhibit 13.1 maps different processes for sourcing or purchasing an item. Depending on the contract duration, transaction costs and specificity (how common the item is) of the product, a firm’s purchasing can be classified into these types of processes:

1. Strategic alliance close relationship

2. Spot purchase no relationship, market based

3. Request for proposal (RFP) requirements are formulated and potential vendors
   prepare a detailed proposal how they intend to
   meet requirements, including a price

4. Reverse auction sellers compete to obtain business, and prices
   typically decrease over time, buyer specifies the
   item

5. Request for bid specification of item is given and price is the main
   or only factor in selecting

6. Vendor managed inventory the supplier manages an item or group of items for a customer

7. Electronic catalog online purchasing

The Bullwhip Effect

The Bullwhip Effect describes the phenomenon of variability magnification as we move from the customer to the producer in the supply chain. It indicates a lack of synchronization among supply chain members. See exhibit 13.2.

Fisher has developed a framework to help managers understand the nature of demand for their products and then devise the supply chain that can best satisfy that demand. Because each category requires a distinctly different kind of supply chain, the root cause of supply chain problems is a mismatch between the type of product and type of supply chain. Functional products are staples that people buy in a wide range of retail outlets, such as grocery stores and gas stations. Because such products satisfy basic needs, which do not change much over time, they have stable, predictable demand and long life cycles. However, there is a lot of competition and therefore low profit margins. Innovative products are products such as fashionable clothes and personal computers that typically have a life cycle of just a few months. The newness of these products makes demand for them unpredictable. Exhibit 13.3 summarizes the differences between functional and innovative products.

Hau Lee expands on Fisher’s ideas by focusing on the supply side of the supply chain. His framework is illustrated in a two by two matrix resulting from low/high uncertainty and low/high demand uncertainty. For visualization refer to exhibit 13.4. A stable supply process is one where the manufacturing process and the underlying technology are mature and the supply base is well established. An evolving supply process is where the manufacturing process and the underlying technology are still under early development and are rapidly changing. Exhibit 13.3 also states the differences between these two supply processes. There are four types of supply chain strategies:

• Efficient supply chains utilize strategies aimed at creating the highest cost
  efficiency

• Risk-hedging supply utilize strategies aimed at pooling and sharing resources
  chains in a supply chain to share risk

• Responsive supply utilize strategies aimed at being responsive and flexible
  chains to the changing and diverse needs of the customers

• Agile supply chains utilize strategies aimed at being responsive and flexible to customer needs, while the risks of supply shortages or disruptions are hedged by pooling inventory and other capacity resources

Outsourcing is the act of moving a firm’s internal activities and decision responsibility to outside providers. This allows a company to create a competitive advantage while reducing cost. Applying to this capability, an entire function (e.g. distribution, manufacturing) or some elements of an activity (e.g. producing parts) may be outsourced. Reasons to move firm’s activities outside the firm can be motivated by organizational and financial factors as well as the factor of improvement. Exhibit 13.5 lists some examples of reasons to outsource.

Logistics is a term that refers to the management functions that support the complete cycle of material flow: from the purchase and internal control production materials to the planning and control of work-in-process to the purchasing, shipping and distribution of the finished product. Outsourcing logistics is for more common nowadays. Exhibit 13.6 is a useful framework to help managers make appropriate choices for the structure of supplier relationships.

Green sourcing refers to the finding of new environmentally friendly technologies and the increasing of the use of recyclable materials. It helps to reduce drive cost in a variety of ways: product content substitution, waste reduction, and lower usage. To transform a traditional process to a green sourcing one, the Six-step process can be applied (see exhibit 13.7):

• Assess the opportunity

• Engage internal supply chain sourcing agents

• Assess the supply base

• Develop the sourcing strategy

• Implement the sourcing strategy

• Institutionalize the sourcing strategy

Total Cost of Ownership

Total Cost of Ownership (TCO) is a financial estimate of the cost of an item, which determines direct and indirect costs of a product or system. It includes all the costs related to the procurements and use of items, including any related costs in disposing of the item after it is no longer useful. It can be applied to internal costs or more broadly to costs throughout the supply chain. The costs can be categorized into three broad areas (see exhibit 13.8):

• Acquisition costs

• Purchase planing costs

• Quality costs

• Taxes

• Purchase price

• Financing costs

• Ownership costs

• Energy costs

• Maintenance and repair

• Financing

• Supply chain/supply network costs

• Post-ownership costs

• Disposal

• Environmental costs

• Warranty costs

• Product liability costs

• Customer dissatisfaction costs

See also example 13.1.

To evaluate supply chain efficiency, two common measures are used: inventory turnover and weeks of supply. The Inventory turnover are the costs of goods sold divided by the average inventory value, whereas the days of supply are the inverse of inventory turn scaled to days.

Inventory turnover: Cost of goods sold

Average aggregate inventory value

Cost of goods sold: cost for a company to produce goods or services provided to customers. This does not include the selling and administrative expenses of the company. Average aggregate inventory value: total value of all items held in inventory for the firm valued at cost. It includes the raw material, work-in-process, finished goods and distribution inventory considered owned by the company.

Weeks of Supply: Average aggregate inventory value * 52 weeks

Cost of goods sold

Weeks of supply is the preferred measure of supply chain efficiency that is mathematically the inverse of inventory turn. See also example 13.2.

Chapter 14