How do firms prioritize and select improvement projects? Unfortunately, the usual method is a combination of guess and chance. Few companies are able to compute the real return on investment from proposed projects, and select the better ones through a rigorous decision process. This is not a trivial problem, but an important one: Many firms spend much money on useless improvements — fixing issues that are non-critical or have low or no effect on factory performance. In these firms, middle managers battle for the attention and investments of senior management by writing up speculative “business cases” for their proposed improvement projects. Few of these firms have heard about Manufacturing Cost Deployment — a structured method for selecting the right improvement project.

What if there was a simple method for estimating the value of improvement projects? Manufacturing Cost Deployment promises to do exactly that. It is a method for identifying the projects with highest return on investment among several potential projects [1]. In other words, it offers a systematic way to cost reduction. Through a seven-step procedure, it assigns the costs of all production losses (or “wastes” in lean lingo) to their root-causes and then calculates returns and costs for potential projects. Although this should be an appealing proposition to decision-makers, Manufacturing Cost Deployment is not widely used in practice and the corresponding literature is sparse [2-3].

Methods for improvement project selection

Companies can use different methods to select and prioritize improvement projects. Most companies use subjective decision-support methods, ranging from pure laissez-faire processes to relying on expert opinion or external advice. There are also a few objective methods, with varying degree of structure and rigor. Among them, variants of the classical cost-benefit analysis (e.g., net-present value) is most popular. In addition, there is a number of sophisticated mathematical optimization models for project prioritization. However, these advanced models find limited use in practice, simply because managers do not understand them.

Not surprisingly, companies that use objective prioritization methods report a higher success rate for improvement projects compared to those companies that exclusively use subjective methods [4]. The lack of structured project selection methods lead to lost opportunities, sub-optimization, and inefficient resource allocation [5]. Neglecting to take a holistic approach to improvement is an important cause for the failure of many continuous improvement initiatives [2]. Hence, a well-designed project selection method should offer a structured process, take a holistic approach, and yet not be complicated. That is what Manufacturing Cost Deployment does.

What is Manufacturing Cost Deployment?

Manufacturing Cost Deployment is a decision-support method proposed by Yamashina and Kubo [1]. Today, it is one of the pillars of the World Class Manufacturing program [6-8]. Its greatest advantage is that it helps assign costs to the root causes of losses in the manufacturing process and sorts the losses according to potential for improvement wins. Manufacturing Cost Deployment increases the legitimacy for projects that yield high investment payoff. Systematically selecting projects that eliminates the root problem—rather than the symptoms, can contribute to a sustained reduction of production costs.

To support the calculations in Manufacturing Cost Deployment, Professor Yamashina has proposed five supporting matrixes [1]. The “A Matrix” identifies and quantifies the losses; the “B Matrix” clarifies cause-and-effect relationships; the “C Matrix” connects losses and manufacturing costs; the “D Matrix” connects causal losses and improvement techniques; and the “E Matrix” identifies benefit values and establishes the cost-reduction program. These matrixes are usually made in commonplace spreadsheet software and are central to the calculations.

A roadmap for Manufacturing Cost Deployment

Manufacturing Cost Deployment can be used following this seven-step roadmap [1, 8]:

• Step 1. Identify loss and waste categories in all processes (for example using Value Stream Mapping).
• Step 2. Quantify wastes and losses (fill the A Matrix).
• Step 3. Establish cause-and-effect relationships (fill the B Matrix).
• Step 4. Assign costs to losses (fill the C Matrix).
• Step 5. Identify improvement projects (fill the D Matrix).
• Step 6. Identify implementation costs and total cost-benefit ratio (fill the E Matrix, and visualize with Pareto diagram).
• Step 7. Select and implement projects (create project plans).

Although the seven steps seem clear and simple, Manufacturing Cost Deployment is an advanced improvement technique, which require maturity and skill. It is not a simple method. In addition, it is very hard to find good and complete descriptions available on how to do it. To help out, we have developed a Manufacturing Cost Deployment Roadmap [9], which is freely available for download at www.better-operations.com/links. We hope you will find it useful.

Pros and cons of Manufacturing Cost Deployment

Decision-support methods such as Manufacturing Cost Deployment make decision-making processes more effective and professional. Additionally, good decision-support methods facilitate learning, understanding, and communication of problems and solutions. However, it is important to remember that even the best methods consist of models that represent simplifications of real-world systems [10]. Managers can use the models to assist and increase the rationality of decisions, but should not let the results of the method dictate the decision. For example, all quantitative models have problems cost-setting soft issues related to human factors and the work environment. Issues related to Health, Safety and Environment (HSE), for example, would usually not be picked up by cost-benefit assessments alone.

A further limitation of Manufacturing Cost Deployment is that it requires substantial resources for rigorous data collection. Successful application requires high-quality input to the model; thus, companies need to develop routines to continuously improve and modify their data collection. Note also that it requires input from both the accounting department and the manufacturing department. Hence, it requires cross-departmental cooperation and substantial training in application. Furthermore, it is perhaps most applicable in relatively stable machining processes, where the underlying calculation model can be maintained for a long period. Hence, we do not suggest that companies should use Manufacturing Cost Deployment as the only method to select improvement projects, but that the method in many cases can give helpful and quantitative input to the project selection process.

As the digitization of manufacturing continues (in what has been termed Industry 4.0), we believe that methods like Manufacturing Cost Deployment will be integrated in business software and easier to apply and maintain. The companies that have already experimented with decision-support methods today are likely to gain a further competitive edge in the future.

References

1. Yamashina, H., & Kubo, T. (2002). Manufacturing cost deployment. International Journal of Production Research, 40(16), 4077-4091.
2. Chakravorty, S. S. (2012). Prioritizing improvement projects: Benefit-Effort analysis. Quality management journal : QMJ, 19(1), 24-33.
3. Høeg, P., & Knutsen, D. (2015). Selection of Improvement Projects. Project Report. Norwegian University of Science and Technology, Trondheim.
4. Kirkham, L., Garza-Reyes, J. A., Kumar, V., & Antony, J. (2014). Prioritisation of operations improvement projects in the European manufacturing industry. International Journal of Production Research, 52(18), 5323-5345
5. Kornfeld, B. J., & Kara, S. (2011). Project portfolio selection in continuous improvement. International Journal of Operations & Production Management, 31(10), 1071 – 1088.
6. Felice, F. D., Petrillo, A., & Monfreda, S. (2013). Improving Operations Performance with World Class Manufacturing Technique: A Case in Automotive Industry: INTECH Open Access Publisher.
7. Chiarini, A., & Vagnoni, E. (2015). World-class manufacturing by Fiat. Comparison with Toyota Production System from a Strategic Management, Management Accounting, Operations Management and Performance Measurement dimension. International Journal of Production Research, 53(2), 590-606.
8. Silva, L. C. S., Kovaleski, J. L., Gaia, S., Garcia, M., & de Andrade Júnior, P. P. (2013). Cost Deployment Tool for Technological Innovation of World Class Manufacturing. Journal of Transportation Technologies, 03(01), 17.
9. Høeg and Knutsen (2016) Roadmap for Manufacturing Cost Deployment, NTNU, Trondheim. Available at https://better-operations.com/wp-content/uploads/2016/07/Manufacturing-Cost-Deployment-Roadmap.pdf. Accessed July 30, 2016.
10. Semini, M. (2011). Applicability of operations research in manufacturing logistics. (2011:213), Ph.D. thesis at the Norwegian University of Science and Technology, Trondheim, Norway.

The authors

• Torbjørn H. Netland is Chair of Production and Operations Management at ETH Zürich, Zürich, Switzerland. He holds a Ph.D. in operations management from NTNU, Trondheim.
• Peter C. H. Høeg is a consultant with Sopra Steria, Oslo, Norway. He holds a Master of Science in industrial economics and technology management from NTNU, Trondheim, with specialization in strategic change management.
• Daniel H. Knutsen is a consultant with Deloitte, Oslo, Norway. He holds a Master of Science in industrial economics and technology management from NTNU, Trondheim, with specialization in finance.