JIT Manufacturing and Supply Chain Fragility

One of the problems with JIT manufacturing is that it is susceptible to supply chain interruptions. Taiichi Ohno, the inventor of JIT/Lean manufacturing, recognized that problem. It is worth quoting in full Goetsch & Davis’s discussion of this issue and Ohno’s solution:

Mass production advocates emphasize that the lines need to keep moving and that the only way to do this is to have lots of parts available for any contingency that might arise. This is the fallacy of just-in-time/Lean according to mass production advocates. JIT/Lean, with no buffer stock of parts, is too precarious. One missing part or a single failure of a machine (because there are no stores of parts) causes the JIT/Lean line to stop. It was this very idea that represented the power of JIT/Lean to Ohno. It meant that there could be no work-arounds for problems that did develop, only solutions to the problems. It focused everyone concerned with the production process on anticipating problems before they happened and on developing and implementing solutions so that they would not cause mischief later on. The fact is that as long as the factory has the security buffer of a warehouse full of parts that might be needed, problems that interrupt the flow of parts to the line do not get solved because they are hidden by the buffer stock. When that buffer is eliminated, the same problems become immediately visible, they take on a new urgency, and solutions emerge—solutions that fix the problem not only for this time but for the future as well. Ohno was absolutely correct. JIT/Lean’s perceived weakness is one of its great strengths. (Goetsch & Davis, 2021, p. 378-379)

According to this, maintaining a buffer stock hides any supply chain issues until the buffer stock is exhausted. This only happens, though, when the buffer stock levels are not monitored. By continually tracking buffer stock – and the rate at which the stock is replenished – any supply chain problems are revealed, and they are revealed at the exact same time that users of JIT manufacturing would notice these shortages. The difference is that the company maintaining buffer stock is not immediately affected, whereas the one using JIT must halt production until the situation is resolved.

The solution Ohno advocates (according to Goetsch & Davis) is that supply chain problems cannot occur (“there could be no work-arounds for problems that did develop, only solutions to the problems”). Problems are avoided simply by having everybody involved working on alternatives to problems that have not yet occurred. Unfortunately, no plan survives contact with reality, and no amount of mental gymnastics will change this, and when there are shortages, Ohno would resolve the issue by having multiple people screaming for a solution. Having multiple people call a supplier pressuring them to resolve a delay does no better than having one person making one call. Phone calls, by themselves, are not sufficient to identify and repair the problem that caused the supplier’s inability to produce needed parts.

One of the workarounds (that Ohno claims is unneeded) to the issue of supplier shortage is to maintain “total visibility – of equipment, people, material, and process” (Kumar, et al, 2013). There are two problems with this: adding such visibility is sure to increase the level of bureaucracy in the supplier, and not all suppliers are willing to allow total visibility. The reason for the latter is that when a company wants visibility into a supplier, it is wanting not only the production rates of a certain part, but also for all the company’s competitors that happen to use the same part.

Akhil Bhargava offers a number of different solutions to the supplier shortage issue. According to him, “The solutions to the traditional mindset of holding Safety stock include Increased data processing involvement in implementation planning efforts in order to upgrade systems to JIT level, statistical process control enhancement to provide timely feedback for engineering and managing tuning, meaningful contingency planning as a response to defects in critical parts, and materials and effective user supply dialogues to support delivery and quality issues.” (Bhargava, 2017). He is basically calling for “better living through IT™”, and none of these solutions actually address supplier shortages, except for the “meaningful contingency planning” option, which is just another phrase for maintaining buffer stock.

The JIT supply chain fragility issue appears to be a problem that has not been resolved and may be unsolvable.

References

Bhargava, A. (2017). A study on the challenges and solutions to just in time manufacturing. International Journal of Business and Management Invention, 6(12), 47-54. https://www.academia.edu/69920210/A_Study_on_The_Challenges_And_Solutions_To_Just_In_Time_Manufacturing

Goetsch, D. L. & Davis, S. B. (2021). Quality management for organizational excellence: Introduction to total quality (9^th ed.). Pearson.

Kumar, S., et al. (2013). Difficulties of Just-in-Time implementation. International Journal on Theoretical and Applied Research in Mechanical Engineering, 2(1), 8-11. http://www.irdindia.in/journal_ijtarme/pdf/vol2_iss1/2.pdf

Benchmarking in High-Security Environments

Benchmarking is certainly important (Goetsch & Davis, 2021), but making it happen in a sector where secrets must be kept involves “creative” solutions, or only making extremely broad comparisons that do not have value to opponents. For example, Gebicke & Magid’s (2010) global study doesn’t compare specific defense systems, but they do compare force size, tooth-to-tail ratio, and so on.

Another type of comparison that doesn’t involve sharing secretive information is between military education institutions. For example, V. Kravets (2024) compares Ukraine’s higher military educational instructions, but instead of comparing technical proficiencies in military science, her goal is to determine the feasibility of including management activities into those institutions. Aren't things going bad enough for Ukraine?

Because of the need to avoid classified information becoming public, the benchmarking that could be used by companies like Rheinmetall AG against BAE Systems or General Dynamics is fraught with difficulties that aren’t encountered in civilian industries. For example, in any company that has IT infrastructure (which means all companies), benchmarking various IT components (like databases or servers) is possible because different industries use the same IT components, and so the benchmarking partners need not be competitors. For example, Google’s Gmail and the fictitious Gaggle dot Com’s GaggleMail are competitors, so no mission-critical information should pass between them. Gaggle dot Com is not a competitor of X (formerly Twitter), so it is OK to benchmark their databases, for example. And this database benchmarking can involve direct comparison of databases made by the same company (like Microsoft) or comparisons of databases made by different companies (Microsoft vs Oracle).

It's not clear whether the same benchmarking would happen in the IT departments of artillery manufacturers since there are all sorts of proprietary IT components. But one can benchmark various systemic quality measures like lean or six sigma standards against other companies, without giving away classified information.

In a 1999 paper by Yarrow & Prabhu, three different modes of benchmarking are presented: metric benchmarking, diagnostic benchmarking, and process benchmarking. Metric benchmarking is the comparison of “apples with apples” performance data. Process benchmarking “involves two or more organizations comparing their practices in a specific area of activity, in depth, to learn how better results can be achieved.” And diagnostic benchmarking “seeks to explore both practices and performance, establishing not only which of the company’s results areas are relatively weak, but also which practices exhibit room for improvement.”

I would like to make guesses of the types of benchmarking done at companies like Rheinmetall AG, without knowing anything about artillery manufacturing! Metric benchmarking could be done on an IT component level (everybody uses databases), but exact benchmarking may be precluded because proprietary software is used. In that case, process benchmarking would still be possible. Diagnostic benchmarking seems to best describe comparisons of six sigma measurements. But I don’t know anything about six sigma, either!

For metric comparisons, then, companies like Rheinmetall AG must enter into consortium agreements with other defense manufacturers as you stated. It isn’t clear how security would be maintained, even if the data is anonymized. In the absence of consortium agreements, one would have to look to different industries. For example, for information about specific tolerances, one would have to compare data from, say, civilian pipe manufacturers. This is probably a one-way transfer of information.

References

Gebicke, S. & Magid, S. (2010). Lessons from around the world: Benchmarking performance in defense. McKinsey & Company. https://www.mckinsey.com/~/media/mckinsey/dotcom/client_service/public%20sector/pdfs/mck%20on%20govt/defense/mog_benchmarking_v9.pdf

Goetsch, D. L. & Davis, S. B. (2021). Quality management for organizational excellence: Introduction to total quality (9^th ed.). Pearson.

Kravets, V. (2024). Development strategies for higher military educational institutions of Ukraine: analysis based on benchmarking. Честь і закон, 2(89), 74-82. https://chiz.nangu.edu.ua/article/download/309198/300732/714412

Yarrow, D. & Prabhu, V. (1999). Collaborating to compete: Benchmarking through regional partnerships. Total Quality Management, 10(4-5), 793-802. https://doi.org/10.1080/0954412997820

Benchmarking in Brick-and-Mortar Stores

According to Jenkins (2025):

Reverse logistics is the reverse of the standard supply chain flow, where goods move from manufacturer to end consumer. Reverse logistics includes activities like returns management, refurbishment, recycling, and disposal. It’s an important part of supply chain management, often involving the return of products due to damage, seasonal inventory, restock, salvage, recalls, or excess inventory.

Benchmarking has several interesting twists when applied to the problems of reverse logistics in brick-and-mortar stores, such as hardware stores.

In the context of a hardware store, internal benchmarking of reverse logistics is possible. For example, comparing rates of customer returns by manufacturer would be valuable to the customer, with the idea of minimizing the number of returns. Based on that information, manufacturers making products with high return rates can be dropped from the hardware store’s offerings (Jenkins, 2025).

In cases where customers do return a product, the speed at which the vendor provides credit can also be tracked. Even if products from the slowest vendor are maintained in the store’s offerings, knowing the expected delay in credit could be useful for accounting purposes. For example, if a particular vendor takes 60 days to provide credit, then that credit cannot be used to cover any expenses until 60 days.

Other information that can be gleaned from benchmarking returns includes measurements to identify and reduce slow-running processes like return processing, reentry into the inventory system, and coordinating with the vendor (Goetsch & Davis, 2021).

Internal comparisons aren’t the only route to using benchmarking for process improvement, of course. Benchmarking partners are also available, and in a way that is different from benchmarking between the information technology (IT) departments of various companies.

By being brick-and-mortar, hardware stores can engage companies in the same business but are separated by geographic distance so that they aren’t direct competitors. For example, a hardware store in Hawaii can form a benchmarking partnership with a hardware store in Philadelphia, say. Because these stores rely on foot traffic, there is extremely little chance that a customer of the Philly store would travel to Hawaii to pick up a hammer!

So, information learned by forming a benchmarking partnership is extremely relevant and valuable to both partners (since they are in the same business), but that information cannot be used against each other (since they are brick-and-mortar stores located on opposite sides of the globe)!

It is also possible to set up a reuse supply chain (Atterblad & Blomkvist, 2023). With this, used and returned products are shunted to a used hardware store. It isn’t clear from Atterblad & Blomkvist how profitable this is to the original hardware store, but it at least avoids a 100% loss. Besides “return to point of origin,” it is also possible for the customer to sell or donate products to a “second-life retailer” (Beh et al, 2016). In that situation, the original hardware store does not benefit at all.

References

Atterblad, R., & Blomkvist, H. (2023). Challenges and recommendations for product reuse: Exploring the reuse supply chain of in-store hardware: A case study. https://www.diva-portal.org/smash/get/diva2:1770268/FULLTEXT01.pdf

Beh, L. S., Ghobadian, A., He, Q., Gallear, D., & O'Regan, N. (2016). Second-life retailing: a reverse supply chain perspective. Supply Chain Management: An International Journal, 21(2), 259-272. https://doi.org/10.1108/SCM-07-2015-0296

Goetsch, D. L. & Davis, S. B. (2021). Quality management for organizational excellence: Introduction to total quality (9^th ed.). Pearson.

Jenkins, A. (2025, 1 May). A guide to reverse logistics: How it works, types and strategies. Oracle NetSuite. https://www.netsuite.com/portal/resource/articles/inventory-management/reverse-logistics.shtml

Change Management Strategy for Converting to Total Quality Management

Introduction

This post follows the steps needed for a company to transition in a total quality management (TQM) operation as outlined in Goetsch & Davis (2021). We begin by listing some of the factors that determine the success of a TQM conversion, and some alternative implementation plans. Next, the implementation process recommended by Goetsch & Davis – the Goetsch-Davis 20-Step Total Quality Implementation Process – is described. The post concludes by describing situations and alternatives when management is lacking commitment to TQM.

Success of Implementing TQM

No two implementations of TQM are the same, and the success depends on several factors. Mann & Kehoe (1993) list several factors besides management buy-in. These factors include the employees’ age distribution, their education level, whether the management uses long term planning, and so on. There are also different approaches to implementing TQM, as described in (Yusof & Aspinwall, 2000, p. 642), which range from companies not implementing TQM all at once, to implementing it on a department-by-department basis. The Goetsch-Davis 20-Step Total Quality Implementation Process described next requires a top-down commitment to TQM throughout the entire company. There is room for adjusting the pace of adopting TQM (determined with the choice of projects deemed fit for “TQM-ization”), but the process is meant to be total.

 

Implementation According to the Goetsch-Davis 20-Step Process

Goetsch & Davis (2021) utilize a three-phase process for implementing TQM. These phases are preparation, planning, and implementation. The details of these phases follow the Goetsch-Davis 20-Step Total Quality Implementation Process (Goetsch & Davis, p. 419-423).

Preparation

As the implementation of TQM is a top-down process, preparation begins with the top executive (CEO for example) becoming committed to TQM. He then forms a Total Quality Steering Committee consisting of the CEO’s direct reports and with the CEO chairing the committee. If a union is involved, the senior union member is also included in the steering committee. This committee is a permanent entity and replaces the former executive staff organization. With the help of a consultant, they engage in team building and get training in TQM’s philosophy, tools, and techniques.

Control then moves to the total quality steering committee. They begin by creating statements of “vision” and guiding principles. Based on those documents they set broad strategic objectives. Next, they communicate and publicize the statements and their plans, and this communication is an ongoing activity by the steering committee.

The steering committee then identifies organizational strengths and weaknesses – why wasn’t that done earlier? As part of this, they identify TQM advocates and TQM resisters. One of these groups of employees could be added to project teams created during the planning phase (guess which one?)

The steering committee will then establish baselines for employee satisfaction and attitudes (performed by the HR department), as well as baseline customer satisfaction. For large customer bases, satisfaction can be determined by using sampling. Customer feedback must include both extremal and internal customers.

Planning

At this point, the steering committee can enter the planning phase! The approach they should use follows the PDCA (Plan-Do-Check-Adjust) cycle, so it may be necessary to return to this step based on the results of what follows. For reference, this is step 12.

The steering committee identifies projects that are amenable (or vulnerable) to adopting TQM. One of the determining factors for the initial choice of projects is the likelihood of success. Teams for each project are appointed. These teams can be cross-departmental, and it is handy to know who the TQM advocates are (Goetsch & Davis, p. 422). The project teams are then trained on TQM principles by members of the steering committee. Finally, teams’ direction is set, and they are activated, each starting their own PDCA cycle.

Implementation or Execution

The project teams then lead the implementation or execution phase. They gather feedback from the team members, the customers, and the employees and report their findings back to the steering committee, perhaps on a monthly basis. (Goetsch & Davis, p. 422) This is the “check” stage of the PDCA loop, and the steering committee makes appropriate adjustments, returning to step 12.

The steering committee modifies organizational structure, procedures, and processes, as necessary. They also implement reward or recognition systems. Finally, union rules are considered.

Conclusion

By following these steps, it should be possible to have a company or organization adopt TQM. If there is no commitment from top management on total quality, then it may be possible to “sell” TQM to them, but…

If enlightenment does not work, it may be time to consider moving on to different employment. That is not always a reasonable option, but long-term prospects for your current employment are not bright either, given top management’s attitude toward total quality. (Goetsch & Davis, p. 423)

Yup, enlightenment.

It also may be possible to implement TQM within a single department. Department total quality is a contradiction since TQM requires commitment from every aspect of the company, but Goetsch & Davis (p. 424) note that this is better than nothing.

For companies with management not committed to adopting TQM, there are other courses of action that can get a company close to using TQM: pursuing ISO 9000 certification and competing for the so-called Baldrige Award.

 

References

Goetsch, D. L. & Davis, S. B. (2021). Quality management for organizational excellence: Introduction to total quality (9^th ed.). Pearson.

Mann, R., & Kehoe, D. (1995). Factors affecting the implementation and success of TQM. International Journal of Quality & Reliability Management, 12(1), 11-23. https://coer.org.nz/wp-content/uploads/2011/09/D22_Factors_affecting_the_implementation_and_success_of_TQM.pdf

Yusof, S. R. M., & Aspinwall, E. (2000). TQM implementation issues: review and case study. International Journal of Operations & Production Management, 20(6), 634-655. https://coer.org.nz/wp-content/uploads/2011/09/D22_Factors_affecting_the_implementation_and_success_of_TQM.pdf

Just-In-Time and Lean Strategies

Introduction

This post discusses the relationship between Just-In-Time (JIT) manufacturing and Lean strategies. We begin by (trying to) define each of these separately, then examine how they work together as JIT/Lean. Next, the relationship between JIT/Lean and total quality management (TQM) is discussed. We conclude by noting that while JIT/Lean strategies seek to advance the goals of TQM, it does not advance all the goals of TQM.

Just-In-Time Manufacturing

Just-In-Time (JIT) manufacturing is a production strategy that minimizes waste by ordering and producing goods on an as-needed basis, directly in response to customer demand. JIT manufacturing is a pull system, so there is no need to rely on forecasts (which is a push system). There is little or no inventory holding costs since production is triggered only when the customer demands it. Besides low inventory holding costs, one of the other advantages to requesting parts only as needed, there is reduced risk of waste in the forms of spoilage (in the case of perishable goods) or obsolescence (for manufactured goods).

Lean Manufacturing

Like JIT, Lean manufacturing is also concerned with reducing waste, but on a broader scale. Goetsch & Davis (2021, p. 377) state that there are seven types of waste that Lean manufacturing seeks to minimize:

• Overproduction
• Wait time
• Transportation costs
• Processing
• Inventory
• Unnecessary motion
• Product defects.

These include wastes not strictly covered by JIT, in particular transportation costs and unnecessary motion.

Comparing the Strategies

It makes sense to combine these two manufacturing philosophies, as they were both invented by Taiichi Ohno (1912 - 1990). As Ohno was employed at Toyota Motor Corporation, the system was initially called the Toyota Production System (TPS) and was seen as an alternative (or refinement) of Henry Ford’s mass production system. As it spread to other industries, it gained the name Lean manufacturing.

Goetsch & Davis do indeed combine JIT and Lean manufacturing, calling it JIT/Lean, which they roughly define as follows:

Just-in-time/Lean is producing only what is needed, when it is needed, and in the quantity that is needed. (p. 376)

This definition doesn’t include the full scope of Lean manufacturing, however.

Combining JIT/Lean with Total Quality Management

JIT/Lean manufacturing integrates well with total quality management (TQM) manufacturing. In particular, by minimizing the production of defective goods, companies following JIT/Lean are concerned with increasing the quality of their goods. Since the system operates only in response to customer demand, product defects are identified early and corrected. Finally, since the JIT/Lean operates as a pull system, it is inherently concerned with customer satisfaction.

This is essentially the conclusion of Cua, McKone & Schroeder, (2001). They combine TQM and JIT together and find that they are compatible with each other as well as with something called Total Productive Maintenance (TPM).

Tesfaye & Kitaw (2017) claim that integrating TQM and JIT are insufficient to guarantee organizational success and requires “interaction between the core company and the external stakeholders (such as governmental organizations, universities, banks, research institutions, and others)” as well as what they call “technological capability accumulation.” This latter refers to transferring and adopting knowledge into the company instead of being “just passive receivers and users of foreign technologies.” (Tesfaye & Kitaw, 2017, p. 22).

The research by Tesfaye & Kitaw (2017) focused exclusively on Ethiopian leather and leather manufacturing companies, but the lack of technological capability accumulation occurs in other industries, even in software companies. Software and IT companies “burn through” technologies at an incredible rate, caused by employee turnover as well as the idea of rejecting older technologies in favor of adopting “the new hotness.”

Conclusion

JIT and Lean are both strategies that improve manufacturing processes. Both are concerned with eliminating waste in such processes, with JIT concerned with minimizing inventory holding costs and minimizing costs that result from spoilage and obsolescence. Lean improves on this by minimizing additional types of waste such as wait times and transportation costs.

JIT/Lean brings the benefits of TQM – improved quality and focus on customer satisfaction – but only to production departments. Companies practicing TQM require continual improvement and customer focus of all departments of a company, whereas JIT/Lean is applicable to production departments. As such, JIT/Lean works well with TQM, but it is distinct from TQM.

References

Cua, K., McKone, K., & Schroeder, R. (2001). Relationships between implementation of TQM, JIT, and TPM and manufacturing performance. Journal of Operations Management 19(6), 675-694. https://doi.org/10.1016/S0272-6963(01)00066-3

Goetsch, D. L. & Davis, S. B. (2021). Quality management for organizational excellence: Introduction to total quality (9^th ed.). Pearson.

Tesfaye, G. & Kitaw, D. (2017). A TQM and JIT integrated continuous improvement model for organizational success: An innovative framework. Journal of Optimization in Industrial Engineering 22,15-23. https://doi.org/10.22094/joie.2017.265

Choosing Key Metrics

The use of Statistical Process Control (SPC) extends across numerous domains, and the key metrics vary by industry. For example, Vetter & Morrice (2019) describe how SPC is used in medical applications. According to them, “Undertaking, achieving, and reporting continuous quality improvement in anesthesiology, critical care, perioperative medicine, and acute and chronic pain management all fundamentally rely on applying statistical process control methods and tools.” Each one of those applications would have their own key metrics.

Goetsch & Davis (p. 305) tell a story about the use of SPC in another industry: semiconductor manufacturing. According to them, a North American semiconductor plant they visited had reduced the number of control charts from 900 down to 100 in a few years. This indicates how it is possible to “overmeasure” processes. The problem here is that the semiconductor plant was collecting too much data. Why is that a bad thing? First, it takes time and money: some poor employer at the plant had to update all those control charts. Second, the charts had to be stored somewhere and that consumes space, even when charts are stored digitally. Finally, all that "overmeasured" data is just noise that masks the signal that is the real information.

Determining exactly what to measure is exceedingly difficult when the processes being monitored are complex. Even when using another control technique, an SPC is also recommended, at least according to Montgomery (2018). For example, SPC is used to augment a system called engineering process control (EPC). EPC is used for industrial processes and is best for situations where the mean value of what is being measured drifts over time. This is completely different from SPC systems, which measures values that vary about a fixed mean.

The data storage problems mentioned in Goetsch & Davis even happen for companies that store their data digitally. For example, web site hosting companies log (record) information about which web pages are requested, which images are downloaded, and any errors that occur. The most common one is the dreaded “404 – page not found” error.

The length of time these logs are retained depend on the industry and the jurisdiction. For example, in the U.S. healthcare industry, HIPAA requirements mandate that logs be maintained for six years. These requirements have been straining smaller web hosting companies because they occupy so much space on hard drives!

As much space as they occupy, log files are extremely valuable! Network engineers and QA specialists pour over these logs not only looking for errors but also to determine statistics about each of the web pages. To do this, there are specialized tools that make finding errors and measuring statistics easy.

Security specialists also use these logs to detect potential security threats. They can detect unauthorized access, identify malware infections, and use this information to respond to security breaches.

One advantage of digital logs over traditional paper-based control charts is that it is possible to create “alerts.” For example, if the log shows that a website went down, an alert in the form of a text message is automatically sent to a network engineer so that he can correct the problem. I imagine that other SPC software systems have a similar feature.

While storing digital logs is expensive, website hosting companies stick to the motto: “store everything, analyze later.” The costs are sometimes worth it.

References

Goetsch, D. L. & Davis, S. B. (2021). Quality management for organizational excellence: Introduction to total quality (9^th ed.). Pearson.

Montgomery, D. et al. (2018). Integrating Statistical Process Control and Engineering Process Control. Journal of Quality Technology 26(2). https://doi.org/10.1080/00224065.1994.11979508

Vetter, T. & Morrice, D. (2019). Statistical process control: No hits, no runs, no errors? Anesthesia & Analgesia 128(2), 374-382. https://doi.org/10.1213/ANE.0000000000003977

When Statistical Process Control Goes Wrong

The role of managers in statistical process control (SPC) is quite important. The manager must set quality standards for his company’s products or services and enforce those standards. This demonstrates an overall commitment to quality and motivates employees to produce quality products and services (Rungtusanatham, 2001). There is the question as to how the enforcement is implemented. Bushe (1988) argues that gradual implementation is more successful than an abrupt imposition.

What I found missing in Goetsch & Davis (2021) was their coverage of management’s duties when things go wrong. For each measurable and tracked quantity, the manager established production quality standards so there is the possibility that the quality can fall below the standard.

In the context of software companies – web hosting companies in particular – there are SPC systems in place, and they are always automated. One of the benefits of automated systems is that text-message alerts can be sent to the appropriate people when some measurement goes out of spec. The “appropriate people” aren’t always managers, but they are usually in the position to effect repairs. Managers are required whenever money is required, however.

A similar situation happens in manufacturing: machine operators would be the first to spot a problem and would most likely be able to repair the machine. If the machine needs replacement, a manager must approve the required funds.

Besides situations needing the expenditure of funds, managers are required when problems arise with supply chain partners. For example, suppose a supplier is providing substandard parts, parts whose quality falls below the agreed-upon quality level. The manager must not allow the quality of his company’s product to suffer as a result.

The manager must work with the supplier to arrive at some solution.

One thing the manager can do is to get an estimate for the time needed for the supplier to resume manufacturing products that are within agreed-upon specifications. Based on this information, the manager may have to delay delivery to his customers or deliver less than what was promised.

In situations when some fixed percentage of the supplier’s parts are below quality standards, the supplier can deliver additional parts in hope that enough of them are acceptable. With additional parts, the company can then deliver quality items to its customers.

The most drastic option is to switch suppliers, either temporarily or permanently. Well-ran businesses will always maintain alternative suppliers, and changing to an alternative supplier would require management decisions.

Managers are not only responsible for setting quality standards, but they are also responsible for deviations from quality standards. By embracing these duties, managers ensure adherence to established product quality standards and sustain customer satisfaction.

References

Bushe, G. (1988). Cultural contradictions of statistical process control in American manufacturing organizations. Journal of Management 14(1). https://doi.org/10.1177/014920638801400103

Goetsch, D. L. & Davis, S. B. (2021). Quality management for organizational excellence: Introduction to total quality (9^th ed.). Pearson.

Rungtusanatham, M. (2001). Beyond improved quality: the motivational effects of statistical process control. Journal of Operations Management 19(6). https://doi.org/10.1016/S0272-6963(01)00070-5