Internal Supply Chain of the Retailer (Retail Supply Chain in the Real World Book 5)

Free download. Book file PDF easily for everyone and every device. You can download and read online Internal Supply Chain of the Retailer (Retail Supply Chain in the Real World Book 5) file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Internal Supply Chain of the Retailer (Retail Supply Chain in the Real World Book 5) book. Happy reading Internal Supply Chain of the Retailer (Retail Supply Chain in the Real World Book 5) Bookeveryone. Download file Free Book PDF Internal Supply Chain of the Retailer (Retail Supply Chain in the Real World Book 5) at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Internal Supply Chain of the Retailer (Retail Supply Chain in the Real World Book 5) Pocket Guide.

Contents

  1. AN OVERVIEW OF DRUG DISTRIBUTION IN DEVELOPED AND DEVELOPING COUNTRIES
  2. Supply Chain Management from a Systems Science Perspective
  3. The Transparent Supply Chain

Phase two - Structure short term planning processes and extend collaboration with suppliers : leverage short-term results with stronger planning process integration with suppliers CPFR - Collaborative Planning, Forecasting, and Replenishment.

AN OVERVIEW OF DRUG DISTRIBUTION IN DEVELOPED AND DEVELOPING COUNTRIES

This is the main topic of the Part II. In the first part of this chapter we highlighted some advantages of APS systems for obtaining superior supply chain plans. In this sense, we discussed the power of these systems, we introduced and discussed some typical systems on the market and we presented three implementation approaches through case studies in large companies. As can be noted, while the current practice and technology allow for dealing with the internal supply chain, the entire supply chain has not been properly considered so far. In Part II we now explore inherent limitations of traditional APS systems in modelling distributed contexts to capture important business phenomena, like negotiation and cooperation, as well as in creating sophisticated simulation scenarios.

To overcome these drawbacks, we introduce what we call a distributed APS system d-APS and we provide some insights about our experience with this kind of system in a Canadian softwood lumber industry. Recent studies in the domain demonstrate that APS is a fruitful field in practice and in academia today. Similarly, it is also a fertile area in the software systems market, with, for example, 44 available software packages having been surveyed by Elliott More recently, McCrea claimed that Supply Chain Management software is facing a sustainable growing market with at least global vendors.

This accounts for the explosion in the market in only five years. This fast-paced dynamism brings about significant market transformation. For example, Lora Cecere, a former research director for AMR Research, discussed the profound changes taking place in the key supply chain technology Cecere, We would like to call attention to some key issues pointed out by this study: need to deal better with risk robustness , agility, responsiveness, multi-tier and focus on relationships.

These can be divided into two major trends: firstly, trying to expand from an internal supply chain point-of-view to an external one, in which relationships with partners and collaborations are considered to a greater extent; and secondly, paying more attention to the stochastic behaviour of the supply chain, managing risks and responding adequately to them. As discussed before, APS procedures are normally used for internal supply chains and collaboration is a complex task. In order to cope with this approach, we will later introduce the distributed APS approach.

In fact, the management of uncertainties is a significant limitation of APS systems Stadtler, The deterministic planning algorithms of the APS systems react quickly to changes while on the other hand, uncertainties are coped with through some limited approaches. For example, by being flexible or having extra capacity , one can absorb non-expected demand from clients. It allows for scenario analysis in stochastic and complex contexts. Basically, as explained by Musselman et al. The advantage is in being able to investigate several variants of a system without disrupting its operations.

Moreover, some vendors provide complete facilities to compare plans and schedules, allowing for multiple copies of different plans visible for side-by-side comparison. Some vendors also provide the ability to produce cost analyses of various planning options. This is a reactive approach, and as a consequence this can lead to nervous planning Van Eck, These sensitivity analysis-type simulations do not necessarily lead the model towards a robust solution Genin et al. If more sophistication is necessary e. Additionally, the integration of a traditional APS system could be made with some discrete-event simulation approaches, such as the one proposed by Lendermann et al.

Within their simulation framework, APS procedures represent the decision system and a discrete-event simulation approach is used to represent the manufacturing and logistics operations. The simulation models of each supply chain member exchange data with the APS in the same way as real manufacturing or logistics nodes. A more pro-active approach is needed to discover solutions that are less sensitive to parameters uncertainties. A way of doing so is to include uncertainties in the model itself so that the algorithms can attempt to find a robust solution Van Eck, Many efforts have been made to overcome this drawback, like the emergence of APS employing stochastic programming, or a special type of this approach called robust optimization.

These techniques combine models for optimum resource allocation under uncertain conditions in order to produce a robust decision-making approach. These are powerful approaches when the uncertainty can be described permitting the evaluation of several scenarios under uncertainties to find the optimum solution. For exemple, Santoro et al. However, at the tactical and operational levels, stochastic programming models problem sizes may still be hard to solve, especially in the APS context and in general real-sized problems Genin et al.

The difficulty is in the growth of the model size when several scenarios are evaluated in a multi-period model. In spite of these drawbacks, stochastic programming is still a promising approach Stadtler, Kazemi et al. Even if stochastic programming-related approaches live up to their promise, traditional APSs will still be restrained by their inability to deal with supply chain relationships, i. For example, in the three examples provided in Part I, collaboration was not considered, mainly due to the inability of the modelling approach and technology being employed.

These are crucial elements in modern supply chain that companies are striving to catch up with. The first question is how to integrate different supply chain partners in a collaborative APS.


  • Advanced Supply Chain Planning Systems (APS) Today and Tomorrow?
  • IN ADDITION TO READING ONLINE, THIS TITLE IS AVAILABLE IN THESE FORMATS:.
  • Book Series: Advances in Business Marketing and Purchasing.
  • Dónde estás, Bernadette (Spanish Edition).
  • Buenas Noches, American Culture: Latina/o Aesthetics of Night.
  • 5 Lessons for Supply Chains from the Financial Crisis - Supply Chain 24/7.

There are possibilities of collaborating in two directions, i. Despite the fact that collaborations are a hot topic today and practitioners and academics alike mention their benefits and potential, in actual factthe notionis quitecomplicated. In theory, one APS for the whole supply chain can be possible, however few companies have.

Most companies are still having trouble achieving the integration of the internal supply chain, as indicated in Part I of this chapter. On the other hand, in theoretical terms collaborations between two APS systems seem to be less complicated. Collaborations can be two-tier e. They can be done in the domains of demand management, inventory management, transportation management, as well as other domains.

Despite this possible collaboration, a real and more profound integration across supply chains through APS systems faces important barriers related to interconnection among business models, which requires sharing strategies, timely information, resources, profits and loss, which can be a quite delicate topic in a very fast and competitive world.

Other gaps exist between APS theory and practice e. However an interesting way to improve simulation and collaboration capabilities of APS systems and contribute to overcoming all these discussed limitations is the concept of d-APS distributed APS systems. Derived from the artificial intelligence field, this concept encompasses different ways of understanding and modelling supply chain planning systems using an agent-based reasoning. The concept of d-APS will be introduced in the next subsection. Distributed advanced planning and scheduling systems hereafter d-APS arise from the convergence of two fields of research.

On one hand, the first field deals with APS, and it generally proposes a centralized perspective of supply chain planning. On the other hand, the second field concerns agent-based manufacturing technology, which entails the development of distributed software systems to support the management of production and distribution systems.

Before discussing d-APS systems, it is interesting to briefly explain what an agent-based system stands for. The agent-based modelling approach aims to build complex software entities interacting with each other using mechanisms from distributed artificial intelligence, distributed computing, social network theory, cognitive science, and operational research Tweedale, ; Samuelson, Examples of this mechanism include: Autonomy : the capacity to act without the intervention of humans or other systems; Pro-activeness : agents do not just act in reaction to their environment, but they are able to show goal-directed behaviour in which they can take initiative; Social ability : agents interact with other agents and perhaps humans beings , and normally they have the ability to engage in social activities e.

This sophisticated social capability is quite interesting in this domain. Examples of these abilities include: Cooperation capability : working together to attain a common goal; Coordination capability : organizing the problem resolution process in a way that makes it possible to prevent problematic interactions and stimulate exploitation of beneficial interactions; Negotiation capability : managing an acceptable agreement for the parts involved, dealing with possible conflicts.

Since the early s, several developments address the context of distributed decision-making across the supply chain using agent technology, but these approaches do not clearly address the integration of advanced planning functions with agents. It models the supply chain as a set of semi-autonomous and collaborative entities acting together to coordinate their decentralized plans. By using the agent—based approach, the concept of d-APS goes farther than traditional APS, as it includes extended capabilities, such as the utilization of negotiation and artificial intelligence mechanisms to coordinate, integrate and synchronize supply chain planning decisions.

In this sense, d-APS systems may provide more modelling functionalities, thus allowing a higher level of complexity to be captured in comparison to classic APS systems. As discussed before in Part I of this chapter, traditional systems have a large hierarchical structure for optimizing different areas procurement, production, distribution, etc. On the other hand, in a d-APS system we have a distributed structure where different agents encapsulate diverse planning functions and work semi-autonomously, interacting with each other following complex social protocols.

In other words, the agents can be seen as a general construct that represents various types of supply chain entities, through which distributed advanced planning tools can be plugged together and collaborate. These entities can be, for example, APS modules for operational planning or for tactical planning Santa-Eulalia et al. Figure 3 schematizes this concept. Agent 1 encapsulates an APS tool dedicated to a specific planning domain 1 e. Agent 1 interacts with 2 , exchanging information or negotiating. Also, the assembler interacts with a set of suppliers and the distributor cooperates with a set of customers.

Each agent has its own specialized APS tool, which can provide solutions for its own planning problem. Each planning problem can be quite different from each other to respond to different behaviours of supply chain partners, such as the ones defined by Gattorna : agile, flexible, lean and continuous replenishment behaviours. The entire supply chain planning takes place when all agents interact with one another collaboratively to reconcile their local plans with the global plan for the entire supply chain.

In Figure 3 we do not represent the control structure of these systems. The reader may have the impression that the relationships between different agents in d-APS are sequential. This figure is a mere representation of the encapsulation of diverse APS tools and the consequent multiple coordination process among those entities, but it does not aim to represent their control structure. In reality, the coordination and control structures of d-APS are quite flexible and do not follow a typical hierarchical system, as in traditional APS systems.

As mentioned by Frayret et al. According to the authors, diverse architectures can be found in the literature to define how the responsibilities are distributed across the organization, such as open architectures Barber et al. Due to this diversity of possible control architectures to manage the interdependencies among activities, diverse mechanisms for coordination exist. Another interesting advantage of d-APS system is related to simulation. Agents are largely used for simulation, since they naturally model the simultaneous operations of multiple agents in an attempt to re-create and predict the actions of complex phenomena.

Thus, simulating actions and interactions of autonomous individuals in a supply chain e. It can naturally generate stochastic behaviours of supply chains like orders arrivals, machines breakdown, etc. A d-APS is composed of semi-autonomous APS tools, each dedicated to a specialized modelling domain, which are normally different in nature from one another, and that can act together in a collaborative manner employing sophisticated interaction schemas.

Despite the fact that APSs are hierarchical systems, d-APS systems can exhibit more complex control structures, where more autonomy can be given to some decision-making entities of the entire planning system. As agent societies, these systems have to perform planning decisions considering both local and global objectives as well as constraints. Furthermore, these systems employ concepts from discrete-event simulation to perform stochastic and dynamic time-advancement experimentations, not only deterministic what-if analysis, as traditional APS do.

These systems incorporate issues from artificial intelligence, including social and local intelligence related mainly to collaboration and negotiation possibilities, learning abilities, and pro-activity.

The New Retail - How Technology, China, and Supply Chains Have Changed the World of Commerce

This is not an exhaustive list, but is the first step towards a more rigorous definition of what d-APS systems are. It is important to mention at this point that this d-APS concept is being used successfully mostly in laboratorial research. However, we strongly believe that it is not far from being ready to reach the market, as some recent industrial experiences demonstrate. In this next subsection we quickly present this concept and how it was tested in industry. It has experts from several domains, including forestry engineering, industrial engineering, mechanical engineering, management sciences such as operations management and strategic management.

FORAC has been working with agent-based systems for supply chain management since The platform was conceived based on a general and well-accepted model for supply chain management, the SCOR Supply-Chain Operations Reference from the Supply Chain Council SCC, ; Stephens, in such a way as to guarantee that the d-APS would be able to solve a large number of supply chain planning problems and be easily used by companies.

This allows the creation of a general agent shell for the d-APS. In order to do so, the supply chain was organized into business units, in which the overall problem is split into smaller sub-problems, which allows that each agent models a smaller scale problem employing specialized planning tools. In order to solve the entire supply chain problem, agents make use of sophisticated interaction mechanisms. Some planning agents have been developed to support a business unit, i.

The following agents are responsible for the operational planning:. Make agents: several make agents are responsible for carrying out production planning functions, each one in charge of a part of the overall planning functions by means of specialized planning capabilities. Several make agents can be used inside a planning unit;.

This architecture can be seen as a general framework that can be applied in diverse fields.

By using dataset from two companies, the research consortium implemented the d-APS schematized in Figure 5. This platform can be used for planning a supply chain, or it can be used for performing simulation with stochastic number generation and time advancement. In what follows, we explain its planning and simulation approach together. Generally speaking, Figure 5 can be understood through its products processing sequence: logs are sawn into green rough lumber, which are then dried, leading to dry rough lumber, the latter finally being transformed into dry planed lumber during the finishing process.

Arrows represent the basic planning and control sequence. Production update : before starting a planning cycle, all planning agents update their inventory level states. The execution agents perform perturbations on the inventory level to represent the stochastic behaviour of the execution system and send the perturbed information back to their respective planning agents. This perturbation in the execution system can be seen as an aggregated representation of what happens on the shop floor, i. It can also be real ERP information from the shop floor. Demand propagation : with the planned inventory updated, all agents are ready to perform operations planning.

The first planning cycle is called demand propagation because the customer demand is transmitted across the whole supply chain. If no products are available in stock, the finishing agent will perform an infinite capacity planning for this demand and will send its requirements in terms of dry rough lumber to the drying agent. The drying agent now performs its planning operations also usingan infinite capacity planning logic, and its requirements in terms of green rough lumber will be sent to the sawing agent.

Then, sawing executes an infinite capacity planning process to generate its needs for logs, which are transmitted to the source agent. The source agent will confirm with sawing whether all requirements will be sent on time. Now, the supply propagation starts. Supply propagation : based on the supply offer from the source agent, sawing now performs finite capacity planning in a way to respect the demand from drying in terms of green rough lumber pull planning approach , and respecting its own limitation in terms of production capacity.

In addition, sawing tries to identify if it still has some available capacity for performing a push planning approach. If there are resources with available capacity, sawing allocates more production based on a price list to maximize the throughput value, meaning that it makes a complementary plan to occupy the additional capacity with products of high market prices.

The sawing plan containing products to answer drying demands and products to occupy the exceeding capacity is finally sent to drying. Drying, in return, uses the same planning logic first a pull and after a push planning logic and sends an offer to the finishing agent. Finishing performs the same planning approach and sends an offer to the deliver agent. Deliver send its offer to the customer agent. In summary, the general idea of the supply propagation is to perform finite capacity planning, where part of the capacity can be used to fulfil orders pull approach and part of it to push products to customers so as to better occupy capacity.

Demand acceptation : the customer agent receives offers from deliver and evaluates whether they satisfy all its needs. Part of this offer can be accepted by the customer and part can be rejected, for example, because it will not arrive at the desired time.

merakimonitor.intello.com

Supply Chain Management from a Systems Science Perspective

This information is sent to the deliver agent. Now, as part of the demand is no longernecessary, deliver will send the adjusted demand for the finishing in the form of a new demand propagation with fewer products. This new demand will be propagated backwards step 2 to the source agent. Next, from source this demand will be forwarded in the form of a supply propagation step 3 up to the deliver agent. During the demand propagation, all planning agents will have more available capacity to be occupied with high market price products. The planning cycle finishes here. Time advancement : due to the fact that the FORAC Platform uses the rolling horizon approach, after the end of a planning cycle involving these four steps, the simulation time moves ahead for the next planning period.

It can vary within any time period, from one day to several months, and it depends on the interest of the supply chain planner. The planning cycle i. These five steps represent the basic logic of the operations planning. Some mechanisms useful for simulation during these five steps are detailed in the following. First, for the production update, one has to understand how the perturbation arrives at the beginning of each planning cycle.

This is explained in Figure 6. Figure 6 shows two situations. It is an ideal world where all plans are executed exactly when they are supposed to be, i. In this situation, at time t , a given agent performs its planning activities resulting in a plan called P t. Plan P t is calculated based on the inventory level of the execution system at t-1 i. I t-1 which is obtained though the Production Update procedure. In a real world situation, uncertainties happen all the time and what has been planned as an inventory level for a given moment is not exactly what is really obtained.

This is due, for example, to machine breakdowns or the stochastic process of the production system. This perturbed planned inventory considered past influence t -1, t -2, It is important to note that the agents try to cope with these accumulated perturbations by adjusting their plans, which is a quite relevant aptitude of supply chain planning and control systems. The reference is the ideal case where no perturbation exists and all agents can determine the optimum inventory levels according to their objective functions and constraints.

To exemplify this mechanism, the graph in Figure 7 shows the results of inventory disruptions i. As one can see, inventory perturbations were introduced at the sawing agent level every 14 days. In this case, every 14 days the sawing agent has to replan all activities to compensate for perturbations. The first perturbation 14 th day was positive, i. The next two perturbations were also positive, while the fourth was negative leading the system to attain the ideal situation.

The remaining perturbations were negative, that is, fewer inventories than planned resulted from the production process. In all cases, it can be noted. Besides manufacturing system perturbations, another relevant supply chain uncertainty Davis, can be modelled in the platform, the demand. The demand agent can generate stochastic demand following a method developed by Lemieux et al. The basic principle consists in randomly generating a total quantity of products for each relation client-deliver-product and for the entire simulation horizon.

Next, products from this total quantity have their delivery dates set stochastically, as well as the date when the demand will be sent to the deliver agent. This stochastic generation can use a seasonality factor, if desired. Two types of typical demand behaviour can be simulated: spot sporadic customers and contract long-term relationship, whose demand cannot be cancelled and penalties apply in the case of late fulfilment.

More detailed information about this mechanism is provided by Lemieux et al. All these perturbations are performed by the platform through a traditional random number generation approach and since a lot of data is needed a fast and flexible generator is employed. The transformation of the random numbers into random variables follows a simple method for discretizing the density function of the probability distribution desired.

Simulation analysts can select different probability distribution functions, such as normal, exponential or triangular. Other important technical information concerns how agents perform their planning activities. Both Demand Propagation and Supply Propagation for each agent are geared up with specialized optimization models.

They are depicted in Table 5 in terms of objective functions, processes and optimization method, according to Frayret et al. The planning approaches described in Table 5 are radically different from each other in regard to their nature, as explained by Frayret et al. The authors mention that the Sawing agent both Demand and Supply Propagations are designed to identify the right mix of log type in order to control the overall divergent production process.

What changes for the demand and for the supply propagation are the objective functions and constraints. Drying, on the other hand, is batch-oriented and tries to simultaneously find the best type of green rough lumber to allocate to the kilns and the best drying process to implement. What is interesting in this approach is that it tries to find a feasible solution in a short time, but if more time is available, it will try to find a better solution using a search algorithm through the solution tree.

Finishing employs a heuristic approach to find what rough dry lumber type will be used and how much should be planed considering setup time. For more details on how planning engines work, the reader is referred to Gaudreault et al. The last issue concerning simulation functioning is the time advancement mechanism used to manage all these uncertain events and planning activities.

We opted for a central simulation clock, which aims at guaranteeing that all agents are synchronized so that none of them are late or in advance. In this case, all agents use the same simulation clock instead of each agent having its own clock. This was used to simplify the time management effort. The general functioning logic is simple. The simulator has a list of all agents participating in. Whenat least one agent is working sometimes more than one could be calculating in parallel ,time advances in realtime.

When all agents are on standby, time advances according to the simulation list. This means that the simulator looks for the next action to accomplish and advances the simulation time until the realization moment of this action. Next, the simulator asks the concerned agent to perform this action. This central clock management mechanism implies that when an agent receives a message involving an action, it adds this action and its respective time of occurrence to the simulation list. This action can be triggered immediately or later, depending on its time of occurrence.

The prototype in the softwood industry was implemented in a large Canadian lumber industry in order to validate the d-APS architecture. The validation was conducted over 18 months of close collaboration with the planning manager and his team. Outputs were therefore validated both, in an industrial context and a changing environment. Two main advantages were identified: the quality of the solution of the proposed d-APS system was superior, and the resolution time was considerably shorter.

This allows the supply chain planner to create several simulated plans quickly.

The Transparent Supply Chain

For example, Santa-Eulalia et al. Cid-Yanez et al. Gaudreault et al. Forget et al. Lemieux et al. Several other developments are being incorporated in this d-APS in order to transform it into the first commercial system in the world employing the distributed planning technology for the forest products industry. This chapter discusses the present and the future of APS systems in two parts. First, in Part I, traditional APS systems are introduced theoretically followed by a discussion of some systems available on the market and, finally, on how APS systems can be properly implemented in practice, according to our experience in the domain.

It is interesting to notice that each solution on the market is different and offers different advantages and drawbacks. Companies desiring to implement such a system have to manage several trade-offs in order to discover the best application for their business requirements, which can be tricky in some situations.

In addition, Part I also discusses three case studies in large companies in order to illustrate the current practice through three typical APS projects: system recovery, system maximization and system readiness. Our experience in recovering APS indicates that implementing such a tool without a structured planning process and without maturity from the company in terms of the seven dimensions of the transformation might lead to project failure.

In terms of APS maximization, system subutilization is normally a symptom of problems related to operating logic, misaligned indicators, unclear roles and responsibilities or a lack of knowledge about the system logic or Supply Chain Management logic. Problems related to the technology are also present, but they tend to be the least demanding.

Finally, in our experience with APS readiness, we discussed and illustrated the importance of making a complete study prior to the system implementation to assure that the company is ready for a transformation path. In Part II we pointed out that traditional technology and practice still have many limitations, thus we explore possible avenues for APS systems.

By highlighting some flaws in traditional approaches in creating sophisticated simulation scenarios and modelling distributed contexts, we introduce what we call a distributed APS system and we provide some insights about our experience with this kind of system in a Canadian softwood lumber industry. Practical experience with this system is producing interesting results in terms of the quality of the solution, planning lead-time and the possibility of creating complex simulation scenarios including complementary possibilities, such as different negotiation protocols between planning entities within a supply chain.

Several improvements are planned for d-APS in order,in the coming years, to deliver the first commercial d-APS in the world employing agent-based and distributed technologies. Help us write another book on this subject and reach those readers. Login to your personal dashboard for more detailed statistics on your publications.

Edited by Dilek Onkal. Edited by Hakan Tozan. We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. Downloaded: In order to do so, this chapter is organized into two parts: Part I — APS Today Section 2 : first, we highlight some advantages of APS systems towards obtaining superior supply chain plans, and in this sense, we discuss the capacity of these systems in employing optimization technology and their ability to integrate time frames ranging from long-term strategic periods to short-term operational ones.

Finally, Section 4 outlines some final remarks and conclusions. Part I: APS today 2. Table 1. A typical implementation project When desiring to start an APS implementation project, it is a good plan to gather insights and advice in the field. They are: Unified Vision : are all stakeholders in agreementas to the expected benefits from the APS project? Case studies In this subsection we present three case studies that aptly represent the following situations: APS Readiness: a company has no APS solution and has decided to adopt one but is doubtful of being ready for it.

Dimension What is Verified? Ideal Reference. Table 2. Table 3. APS Readiness Result. Table 4. APS Readiness Scale. The root causes identified were: Lack of Supply Chain Management concepts in the organization. Lack of an adequate product hierarchy across all planning processes. Lack of alignment between their KPI structure and their supply chain strategic objectives. The demand for six-sigma 1 and other quality initiatives is an emerging trend.

The aerospace industry, among others, will almost certainly require improvements in supply chain quality as OEMs and prime contractors work towards the goal of producing defect-free work on the first try. It is a fundamental premise of manufacturing that high-quality end products cannot be built cost effectively from low-quality components. Most suppliers operate in a tolerance range of two to three sigma. OEMs cannot achieve six-sigma quality with three-sigma suppliers.

Sigma is a statistical measure of the capability of a business or manufacturing process to perform defect-free work. The common measurement index is defects per unit. A unit can be virtually anything e. At the six-sigma level, the incidence of defects is nearly zero Velocci, Supply chain integration requires that quality be more than a set of abstract standards. Quality must be a systemic way of doing business that is instilled in all participants in the chain.

Quality has become critical in supply chains using just-in-time manufacturing with low inventory levels because they have very few buffers to protect against quality failures. SMEs should not consider quality only as a requirement for continued supply chain participation, but as a strategic capability. SMEs that adopt quality as a competitive strategy are finding that they are better able to weather cyclical swings in their businesses and that their product costs are lower.

Thus, SMEs may reap benefits by exceeding the quality levels required by supply chains. Most integrated supply chains require that participants have a carefully reasoned and executed quality plan that includes concerted efforts to provide levels of quality appropriate to the market being served. Proficient problem identification and problem solving capabilities are fundamental elements of the quality plan.

Although six sigma and other quality programs may be of strategic benefit, they can be expensive to implement. Thus, SMEs must carefully target and prioritize improvements in terms of their effect on the company's operational and financial goals, as well as overall business objectives. Delivering a quality product requires, at a minimum, well established and well documented manufacturing processes and controls that meet impartial standards and customer requirements. Six-sigma is one such standard, but other, less exacting standards may be adequate.

SMEs are increasingly being required to identify, capture, analyze, and act on process data in conformance with SPC. SMEs should discuss with their supply chain partners how quality improvements can affect the overall performance of the supply chain. Together, the partners should identify and prioritize SME actions that will have the greatest impact on overall supply chain quality, cost, and cycle time and determine how these actions will translate into increased competitiveness and profitability for the SME. Properly implemented quality procedures can reduce rework, scrap, testing, and inspection and improve on-time deliveries.

The result can be substantial savings and fewer schedule variances. For example, in the development and pilot production phases of new electronic products, two new quality techniques, highly-accelerated life testing HALT and highly-accelerated stress screening HASS have yielded substantial benefits. Although they are somewhat expensive, these techniques have been shown to be effective in debugging new products and identifying. In many cases, these new techniques have been better able to identify problems in advance of full-scale production than previous methods, including MIL Standard tests.

SPC has advanced beyond its early role as an after-the-fact application of statistics to production and inspection data, when it served primarily as a means of creating a report verifying compliance with customer requirements. Today, SPC can provide opportunities for real-time assessment of manufacturing processes and can enable response to the causes of process variations as they happen. Thus, processes can be adjusted before more nonconforming products are produced. The savings are immediate and quantifiable, not just in direct costs, but also in more timely shipments, improved product quality, and increased customer satisfaction, all of which reflect favorably on SMEs seeking long-term supply chain partnerships.

Participants in a supply chain need a common language to facilitate accurate communication on issues of quality. Because such languages are not universally defined and can vary from chain to chain, quality standards, such as ISO, can be helpful. SMEs may wish to adopt such standards voluntarily. SME participation in integrated supply chains can facilitate quality improvements through the exchange of ''best practices" among partners, which can enhance understanding and provide examples of proven techniques. More advanced participants in the chain can assist those who are less advanced to adopt and use appropriate quality techniques.

In response to the requirements of integrated supply chains for improved quality, small and medium-sized manufacturing enterprises should adopt quality as a competitive strategy and consider implementing techniques, such as six sigma, ISO certification, and statistical process controls, to comply with customer demands, improve overall business performance, and provide a common language for communication on quality issues. Global bidding on the Internet has forced suppliers in many industries to slash prices dramatically. Costs have always been critical, and in the increasingly global economy it is not unusual for SMEs to find sudden gaps between their prices and the prices of competitors from low-cost areas.

The convergence of 1 improvements in high-speed communications, 2 reduced transportation costs, 3 widespread adoption of. English as the language of business, and 4 universal access to technology and effective management practices has enabled companies in areas with low labor costs to become competitive regardless of location. Thus, many SMEs must substantially reduce costs to remain competitive, and they are finding that competing on the basis of cost alone is becoming a losing game.

In some industries, geographic proximity is no longer an advantage. The Internet and modern transportation capabilities have combined to enable on-line businesses with low labor costs and appropriate capabilities to compete from anywhere in the world. These capabilities have eliminated two traditional advantages of local suppliers: their physical proximity and customer ignorance of comparison prices.

Large OEMs, including the Boeing Company and United Technologies Corporation UTC , are taking advantage of these trends by turning to on-line bidding for the procurement of low-technology, pre-engineered items, such as nuts, bolts, and steel shafts, for which there are a large number of suppliers. Several companies have sprung up to conduct online auctions that pair worldwide buyers and sellers.

Although price is important, buyers may consider other factors in their final decision or may reject all bids. New suppliers are required to demonstrate appropriate capabilities prior to bidding. Unless the bids of new suppliers are substantially below those of incumbent suppliers, the jobs may go to incumbents because the buyer is more familiar with their capabilities or wants to retain a small base of the most competent suppliers. In , for example, FreeMarkets structured a daylong bidding event for UTC that included numerous lots of simple machined metal parts. Prior to the bidding, FreeMarkets analyzed a list of preapproved suppliers and selected ones acceptable to the buyer.

The buyer was able to specify preferences, such as the inclusion of small and disadvantaged businesses. Each supplier was sent a package in advance detailing the parts being sought, pertinent quality requirements, and delivery dates. In this example, the bidding was conducted on a secure network. Suppliers were not informed of the names of their competitors or the prices paid for similar items in the past. They could, however, see rival bids in real time. Bidding was described as "bare-knuckled," with low bids coming from qualified suppliers in India and elsewhere.

In one lot, an incumbent supplier was forced to reduce its price by more than 50 percent from its previous contract to retain the business. These and other auction services are opening new worldwide markets to SMEs that have competitive costs and capabilities. Once they have become qualified suppliers, only a PC and a modem are required to participate. However, Internet bidding can dramatically reduce the profit margins of SMEs that have not properly positioned themselves in terms of cost, product differentiation, or added value.

Because few SMEs have sufficient margins to withstand such competition, it is essential for their survival that they prepare in advance for such eventualities. Opportunities for internal cost reductions include direct labor, materials, scrap, and rework. Other opportunities can be found through creative reductions in overhead. The Boeing Company, for example, reports saving hundreds of millions of dollars by moving its system of spare parts sales and aircraft maintenance manuals to the Internet.

Techniques, such as just-in-time manufacturing, activity-based costing ABC , vendor-managed inventory, and lean manufacturing, can be used effectively to reduce non-value-added costs. Traditional accounting systems accumulate costs, such as engineering and material handling, into overhead accounts, which are then allocated to products based on the amount of direct labor each product requires.

This approach is useful when there are long manufacturing runs and direct labor is a large part of total costs. However, traditional accounting systems are less useful for firms with substantial investments in information, product design, and agile manufacturing technologies.

ABC assigns job costs based on the actual use of resources, enabling firms to price their products appropriately, determine in which markets they can compete effectively, make better capital allocation decisions, and calculate the incremental costs associated with potential courses of action. Just as OEMs use outsourcing, SMEs must consider creating and integrating their own supply chains to optimize their own cost structures. This potential for cost improvement has yet to be exploited by most SMEs.

Participants may, for instance, reallocate work among themselves to improve the overall efficiency of the supply chain. Several personal computer companies have implemented "channel assembly," delegating responsibility for final assembly to distributors with specific customer knowledge and lower labor rates. This practice can be used to reduce inventory levels and the probability of obsolete inventories.

Deloitte Resources

Other supply chains have succeeded in redeploying, consolidating, or sharing warehouse space and inventories among participants to reduce overall costs to the chain.