Implementation Issues and Processes

One of the primary advantages of S-DBR over the traditional DBR is in the speed of implementation and results. Implementing S-DBR should always start with choking the material release so that only orders to be delivered in the horizon of the production time buffer would be found in the shop floor. We've already mentioned that a good initial estimation of the time buffer is one-half the current production lead time. If it is not clear what the current production lead time is, then take the standard lead time in this industry and cut it to half.

A few exceptions to the half-the-production-lead-time rule exist. The first is an environment of a dedicated assembly line, where all the WIP in the line is restricted to several hours. The other exception is where real effective Lean methods have vastly reduced the WIP and lead time. In those cases, the production buffer can be based on the current production lead time for implementation.

Choking the material release must include dealing with the current batching policies by either abolishing them by making the customer order quantity the batch size or at least reducing the batch size.

The next mandatory move is to establish BM. This move can be done manually or be supported by software. Putting red labels on the red orders is a simple visual tool for an initial implementation of BM. For the operators in the shop, the rules of behavior with red orders must be absolutely clear: The workers must take responsibility to flow the red orders to completion. If an operator needs materials, tools, drawings, or anything else required to move a red order, he or she must get whatever is needed or notify production management immediately of the support needed. Overtime is another option of production management to deal with red orders.

Implementing the load control function generally takes a little more time. Having the planned load in place is not mandatory for getting the initial results. Actually, it is not even very urgent to identify the "weakest link" (the relative CCR). The assumption is that the demand would not rise very quickly, and thus choking the release based on the production buffers and the simple priority rules are enough to improve the due date performance and stabilize the shop.

Once the implementation stabilizes the shop, then identifying the CCR is easy enough. It is the resource that most of the time holds the longest queue of WIP as measured in processing time at that resource. Then the initial steps to implement planned load can be taken. If more than one natural candidate for the CCR shows up, then monitoring the load on three to five work centers is good enough. Once good data on the planned load is obtained, the identity of the real CCR becomes clear. If more than one CCR exists in the same flow, then determine logically which should be the one to use and increase the capacity of the other.

The next step is to establish the rules for Sales to quote due dates, which considers the safe dates given by the planned load (plus half the production buffer). Now the implementation is ready to face a real increase in sales.

The process of ongoing improvement (POOGI) should be established at this point. The idea is that every time an order becomes red, a reason should be entered by a person in charge from the production management personnel. The reason is taken from a prepared table of possible reasons. A reason must answer the question "What now delays the order?" The list of reasons (such as "Quality problems are identified and being taken care of," "Huge queue of work at work center X due to a long machine breakdown," "Work center X currently works on the order," etc.) is presented weekly as a Pareto list and a team under the direction of production management should look to eliminate the top causes of lateness on the list. This procedure should improve the flow even more and then efforts to capitalize on it by creating offers that are more lucrative to the market should be taken.

Looking Ahead to MTS

This chapter focuses on MTO environments. DBR, like previous production planning methods, has assumed every production order must have a due date and that due dates determine the relative priority of any production order. The next chapter is going to show that this assumption is not necessarily true and actually, there should be a clear distinction between MTO and MTS, where no definite customer order exists at the time of material release to production. The next chapter will also deal with mixed environments where certain products are MTS, while others are MTO.

Suggested Reading

Schragenheim, E., Dettmer, H. W., and Patterson, W. 2009. Supply Chain at Warp Speed. Boca Raton, FL: CRC Press. Chapters 3 through 5 are especially relevant. www.inherentsimplicity. com/warp-speed is a site that allows downloading of the MICSS simulator including analysis files and more related materials.

References

Blackstone, J. H. 2008. The APICS Dictionary. 12th ed. Alexandria, VA: APICS.

Fry, T. D., Cox, J. F. and Blackstone, J. H. 1992. "An analysis and discussion of the OPT software and its use," Production and Operations Management Journal 1(2)Spring: 229242.

Goldratt, E. M. 1990a. The Haystack Syndrome: Sifting Information Out of the Data Ocean. Crotonon-Hudson, NY: North River Press.

Goldratt, E. M. 1990b. What Is This Thing Called Theory of Constraints and How Should It be Implemented? Croton-on-Hudson, NY: North River Press.

Goldratt, E. M. 1997. Critical Chain. Great Barrington, MA: North River Press.

Goldratt, E. M. 2009. "Standing on the Shoulders of Giants," The Manufacturer. June. http://www.themanufacturer.com/uk/content/9280/Standing_on_the_shoulders_of_giants. (accessed February 4, 2010).

Goldratt, E. M. and Cox, J. 1984. The Goal. Croton-on-Hudson, NY: North River Press.

Goldratt, E. M. and Fox, R. E. 1986. The Race. Croton-on-Hudson, NY: North River Press.

Schragenheim, E. and Dettmer, H. W. 2000. Manufacturing at Warp Speed: Optimizing Supply Chain Financial Performance. Boca Raton, FL: CRC Press.

Schragenheim, E., Dettmer, H. W., and Patterson, W. 2009. Supply Chain Management at Warp Speed. Boca Raton, FL: CRC Press.

Schragenheim, E. and Walsh, D. P. 2002. "The critical distinction between manufacturing and multi-projects," The Performance Advantage, February, pages 4246.

Sullivan, T. T., Reid, R. A., and Cartier, B. 2007. TOCICO Dictionary. http://www.tocico.org/? page=dictionary.

About the Author.

In the last 25 years, Eli Schragenheim has taught, spoke at conferences, and consulted in more than 15 countries, including the United States, Canada, India, China, and Japan. He has also developed software simulation tools especially designed to experience the thinking of TOC, and consulted to several application software companies to develop the right TOC functionally in their own packages.

Mr. Schragenheim was a partner in the A.Y. Goldratt Institute and he is now a Director in The Goldratt Schools.

He is the author of Management Dilemmas. He collaborated with H. William Dettmer in writing Manufacturing at Warp Speed. He also collaborated with Carol A. Ptak on ERP, Tools, Techniques, and Applications for Integrating the Supply Chain, and with Dr. Goldratt and Carol A. Ptak on Necessary But Not Sufficient. In March 2009, a new book titled Supply Chain Fulfillment at Warp Speed, with H. William Dettmer and Wayne Patterson was published. The new book contains much of the new developments of TOC in operations.

Mr. Schragenheim holds an MBA from Tel Aviv University, Israel, and a BSc in Mathematics and Physics from the Hebrew University in Jerusalem. In-between his formal studies, he was a TV director for almost 10 years. He is a citizen of Israel.

CHAPTER 10.

Managing Make-to-Stock and the Concept of Make-to-Availability

Eli Schragenheim

Introduction.

Is there a basic difference between producing an order for a specific customer order and producing an order in anticipation of future demand? From a business perspective, there is an obvious difference: producing in anticipation of demand means risk, while producing to a firm order looks safe enough. However, once there is a decision to produce to stock, either based on formal forecasting or on a hunch, should there be a difference in the rules behind production planning and execution?

The traditional approach does not see much of a difference between make-to-stock (MTS) and make-to-order (MTO) for production management. Thus, mixing within the same work order a quantity that is covered by firm orders with a quantity based on anticipation is very common.

When Drum-Buffer-Rope (DBR) (Goldratt and Cox, 1984; Goldratt and Fox, 1986) was developed in the 1980s, it did not challenge the assumption that there is no difference in planning the shop floor for firm orders and planning it for anticipation of future demand. In addition, Buffer Management (BM) did not see any difference between MTO and MTS.

This chapter argues that there should be a difference. It is designed to explain the logic of why different rules, both in the planning and in the execution, are required and goes on to detail the method itself and its ramifications.

While dealing with the topic of MTS and how it is different from MTO, another insight by Dr. Goldratt has emerged that led to a new term called make-to-availability (MTA), where we add to the operational meaning of MTS a marketing message: We commit to our chosen market to hold perfect availability of a group of specific end products at a specific warehouse. The objective of MTA is to offer a new business opportunity based on providing extra value to clients through guaranteed lead times, which competitors will find it hard to imitate.

Copyright 2010 by Eli Schragenheim.

In this chapter, we explain the operational ramifications required to offer this commitment to the market. We do not go into the marketing side1 of how such an offer could be used to enhance the perception of value of the client and how to capitalize on that added value to gain more profits to the organization.

The chapter deals with why there is a need to change to the Simplified Drum-Buffer-Rope (S-DBR) methodology and the related BM mechanism, presented in Chapter 9, to deal with MTA. Then we present the methodology itself, both the planning and the BM rules. Following that we deal with some broader issues of MTA, like managing seasonality and mixed environments of both MTA and MTO or cases that are MTS rather than MTA. Toward the end of the chapter, we highlight some practical implementation issues.

Why Is a Special Methodology for MTS Required?

Two different parameters are usually considered in evaluating planning the production of MTS. One is determining the quantity to be produced and the other is fixing the date for the shop floor to complete production.

Is anything a little bit strange in the second parameter?

When a client submits an order, the due date is important. Does the client truly need the order on that date? Moreover, even if the client needs some of the ordered quantity at the agreed upon delivery date, in most cases not all of the quantity is required at that time. Still, he has the right to expect delivery at the agreed upon date and missing that date can cause negative effects on the reputation of the supplier. Therefore, it is natural that efforts should be made to deliver all firm orders on time.

Is it really the same in producing to stock?

The required quantity in producing to stock is an estimate. The chosen quantity to produce is not likely, in most cases, to be consumed at the date given to the production order. Therefore, the date simply sets the priority for the order and the performance measurement for the shop floor.

Let's see if this date is good enough for setting the internal priority in the shop floor. What truly dictates the priority of a production order for stock? In most cases, production to stock aims to provide availability of the item to any urgent order. In such a case, the true priority of the production order has to be dependent on the availability of finished stock for that particular item. Will stock be there for the urgent order? In addition, if the due date performance for MTS orders is in most cases not very critical, should we make it the prime performance measurement?

Our conclusion is that for MTS there is a need to redefine the priorities for the shop floor and base the appropriate performance measurements on that. This means we might have to develop a different BM scheme for MTS.

There is one case though where an MTS order has to be completed at a certain meaningful date. This is when the stock is to provide availability at a date where we anticipate a significant demand, like a holiday or the first day of an advertised promotion. In this type of MTS, the date is very important. However, in all other cases, certainly when the point is to support continuous availability of the items, the required date has no special meaning.

The decision on what quantity to produce to stock is also quite different from MTO. The Theory of Constraints (TOC) is focused on generating Throughput, which is not the same as generating output. Therefore, while in MTO the client's wishes, as expressed by the firm order, dictate both the quantity and the completion date and directly results in Throughput, for MTS we need another approach.

The Current Confusion in Managing Stock

The current practice in production management inter-mixes MTO and MTS. Economic Order Quantities (EOQs) lead production planning to fill the demand for current customer orders and then add stock intended to cover future orders. This combination of customer orders and stock orders executed in a material requirements planning (MRP)2 environment uses the problematic notion of the "available to promise" algorithm. This algorithm helps in deciding whether current requests can be reasonably met in quantity and time. The problems with this algorithm3 are twofold: the first is the unreliable way uncertainty in the shop floor is handled, and the second is inconsistency due to the varying levels of stock that is not already assigned to firm orders. From the potential customer point of view, sometimes an order is delivered very soon and sometimes an order is delivered relatively slowly. This is problematic because there is no standard for the customer to rely on.

What makes the mix between orders and stock even more confusing is that in MRP every pass from one level to the next level in the bill of materials (BOM) has its own work order, which often merges the requirements from several customer orders and then inflates the work order even more by adding items for stock. As the expected customer demand changes at the top level, those fluctuations are then exploded to the lower levels in the BOM structure with each new iteration of MRP (often done weekly) thus impacting the ratio between the parts that are required for firm orders and parts that are for stock. This means how much component stock for future parts has been added is arbitrary and not derived based on a calculated decision to maintain a certain level of stock of a specific component. In this way calculating the "available-to-promise," looking for the available stock of a large number of components, is very tricky indeed. It could easily be that for a certain end product some of the required components have a lot of stock, while other components are short. MRP developers have tried to treat the effect of this nervousness by providing pegging (Blackstone, 2008, 97 ) "to determine requirements traceability, which allows one to trace the source of requirements through record linkages." ( APICS 2008, used by permission, all rights reserved.) Another source of confusion is the reliance on forecasting, or rather the common misunderstanding of how to use forecasts to support good decisions.

The Common Misunderstanding of Forecasts

The forecasting algorithm is not a prophecy and was never intended to answer questions like, "How many units will be sold next month?" Forecasting is a statistical model that describes, under certain assumptions, a specific uncertain future behavior of a specific variable. Being just a statistical model means all it can do is point to a possible spread of results treated in a solid statistical way-finding a probable average and a probable standard deviation around that average. By providing this partial information on the possible range of results, it allows the decision maker to consider where in the range it is best to place the quantity in question for minimum risk.