Measuring the Value of the Supply Chain

Linking Financial Performance and Supply Chain Decisions

Enrico Camerinelli

• Publisher: Routledge
• Year: 2016
• ISBN:
  9781317099062
• Edition: 1
• Topic: Commerce
• Subtopic: Management

In a company ecosystem the supply chain manager is tasked with duties and objectives primarily aimed at controlling and reducing costs, while optimizing the material flows. Yet, in many organizations, common perception limits supply chain management to product logistics, materials handling and warehouse management. The supply chain manager must learn how to communicate the results of his work to show the importance and impact supply chain management operations have on a company. In this book, Enrico Camerinelli provides the supply chain manager and the chief financial officer with the means to link the value of the supply chain to an organization's bottom line. He explores the problem with current supply chain metrics, shows how to close the gap between financial decisions and supply chain performance, suggests a model to provide a lingua franca for supply chain, financial and other managers throughout the company and points to ways in which new technology can help measure the value of the supply chain. Using case studies and interviews with supply chain and financial experts, Measuring the Value of the Supply Chain will help financial and supply chain managers achieve strategic advantage through effective supply chain management.

PART I

Current Thinking on Supply Chain Measurement

1
Supply Chain Management Today

The supply chain is playing an increasingly important part in defining companies’ competitive positioning and, ultimately, their success in the marketplace. Consequently, in most organizations, the role of supply chain management is growing rapidly, as is awareness of this role. However, many companies remain unclear about precisely how to define supply chain management or how to integrate it with those parts of their business that are conventionally associated with the generation of revenue. This lack of clarity means that, understandably, organizations find it difficult to optimize their supply chains in response to the rapidly changing dynamics of today’s markets.

The questions that senior managers are asking themselves about supply chain management not only indicate their strong desire to close this gap in understanding, but also highlight shortcomings in the methods they currently use to analyze their supply chains.

Typically, they want to know:

• What do supply chains look like?

• Which elements of each supply chain should my company own rather than have supplied as a service by another company?

• What are the typical cycle times for each stage of our supply chain?

• What are the typical cash cycles associated with our supply chain?

To begin to answer some of these questions and show that organizations need to adopt a more sophisticated approach to supply chain analysis I will look at how what we now describe as supply chain management has come into being. In doing so I will show how the trends that have shaped it in the past are still playing out today. In today’s successful enterprises the greater emphasis placed on supply chain management is not only changing the approach towards the familiar processes of forecasting and sales and operations planning (S&OP), but is also redefining the concept of visibility to deliver more detailed insight into how best to bring products and services to market. We will also see that technology will play a key role in defining the future of supply chain management.

A Brief History of Supply Chain Management

Managerial attitudes towards defining supply chain management have always been highly dependent on market conditions and the imperatives that drive their organizations’ strategic goals. The same is true in today’s increasingly dynamic markets.

The integration of logistics processes, which ultimately gave birth to what we now term supply chain management, is nothing new, as observed by Dr Donald Bowersox, University Professor and Dean Emeritus at Michigan State University, who describes it as the third stage of evolution in a process that started in the 1960s when – despite the emergence of physical distribution management in the 1950s – there was a highly fragmented approach to logistics.¹

At that time companies began to realize that changing their organizational structure could reveal opportunities to reduce distribution costs. This led to managers increasingly becoming responsible for inventory control, and, from this point on, approaches to physical distribution matured quickly, with an emphasis on customer service changing the demands on logistics management. A greater understanding of how logistics affects cash flow encouraged efforts to integrate distribution with materials management.

Integrated logistics management took off in the 1980s, with organizations bringing together inbound and outbound processes. Although they were driven primarily by the desire to reduce costs, their efforts also gave rise to the concept of the value chain as a tool for enabling the kind of strategic planning that could deliver competitive advantage. Following the logical course of evolution of logistics management, supply chain management, as it is known today, then developed in the 1990s when organizations began to look at end-to-end processes, trying to break down the internal barriers, in order to create what has become known as the extended enterprise.

At its most basic, the supply chain is viewed simply as the process by which products or services are brought to market. A useful description of supply chain management comes from Martin Christopher, Professor of Marketing and Logistics at the Cranfield School of Management, who defines it as follows:

Supply chain management encompasses both the internal management of the logistics processes that supports the flow of product and related information, as well as the upstream and downstream linkages with suppliers and customers.²

Viewed within this broad definition, the supply chain can take many forms depending on the structure of a business, the nature of the industry in which it operates and its strategic goals. Many organizations can be links in this chain – raw materials suppliers, contract manufacturers, third-party logistics providers, or transportation and warehousing companies, to name but a few. This broadening of the context in which companies view their supply chains can help them recognize how they interact with other business processes and, therefore, how they can add more value than is currently perceived.

Market conditions are far more dynamic now than at any time in the past. Looking at supply chain management in a wider context – one that takes into account the manner in which markets are evolving – will certainly help supply chain managers answer their questions about their supply chains, but may also help them better articulate the very questions they ask.

The Supply Chain Perspective on Forecasting

For most companies, forecasting – the analysis of current and historical data to determine future market trends – is a familiar activity that remains extremely important in enabling them to achieve their strategic goals. It is generally applied to demand patterns in order to define customers’ potential future behaviour and has become a prominent focal point for most enterprises, as they need to know well in advance the likely demand profiles they will face. In developing their forecasting techniques companies have worked to shorten the time horizon as far as possible so that they can make earlier and more accurate predictions, thereby giving themselves more room for manoeuvre when preparing and planning their business strategies.

From a supply chain management perspective, however, it has become clear that forecasting should not focus solely on predicting demand as it has often done in the past. Instead, enterprises need to apply more accurate forecasting techniques to their supply response. In this way they will be able to position themselves better to cope with the increasingly complex, dynamic and global markets in which they must operate.

Current and historical data analysis should inform supplier management, production operations, logistics, transportation and returns management. At present it is generally assumed that customer demand forecasts are the only information needed and that data on sales volumes can be broken down into predicted volumes for the other domains through correlation tables. Such an assumption holds true in stable environments where demand can be modelled using stochastic modelling techniques based on cause-and-effect correlations. In reality, however, the current business world is far more unpredictable than such methods allow for and cannot be easily framed within a stable model.

Some argue that unpredictability is nothing new and that it has always been a feature of any market; hence the creation of forecasting techniques in the first place. That is certainly true to an extent, but the unpredictable information that has been taken into account in the past related mainly to the quantity of goods an enterprise needed to sell. In today’s more dynamic business environment there are several other unpredictable factors to take into account, namely:

• multiple product categories

• associated services

• rapidly changing consumer tastes

• price rebates due to stiff – and often unfair – competition from countries in the Far East.

Since any deviation of the actual values from those forecasted could result in a huge negative impact on company profitability, companies must address these new variables and can do so in several ways. For example, they could shift away from centralized and automatic algorithm-based planning and forecasting towards an increasingly collaborative approach. Shared knowledge about market conditions, domain expertise and common sense now provide the results that were once automatically calculated by complex, multi-level algorithms. Enterprise application software that enables collaboration, dialogue and opportunity-based decisions now often sits beside the rigid and mechanical computation modules that have traditionally been used in the past. Forecasting software is therefore a support mechanism for the human-based, collaborative decision-making process.

Sales and Operations Planning

Having recognized the new demands on forecasting, the next step is to consider how to analyze current data to develop and implement specific courses of action for future operations over a specified time period. In other words, we move from forecasting to planning, which requires that enterprises examine the entire spectrum of their supply chain processes and thereby derive its value.

There is evidence that many enterprises are already looking to move beyond simple forecasting based on algorithms towards more collaborative planning methods. Many are looking with great interest at how to develop sales and operations planning. In most cases, Sales and Operations Planning (S&OP) is performed monthly, with senior management teams balancing profitability objectives, channel requirements and the organization’s overall business strategy to decide how best to balance demand with supply, using such tools as a consensus-based demand plan, a constraint-based supply plan and an agreed process to bring the two together. This approach enables them to focus on the most profitable customers or, at the very least, on serving them more effectively.

Sales and Operations Planning basically drives a profitable balance between demand and supply. It helps to provide a vital understanding of important measures, such as the actual level of customer demand, which sectors of that demand are more or less profitable, and what constraints on supply a company faces. It also emphasizes the importance of establishing a collaborative demand plan across the many departments within the organization and involves customers in the definition of this plan. Good S&OP processes have been important for many years, but the decisions that companies must make based on such planning have become significantly more complex. Customers are more demanding, the number of distribution channels has grown, demand schedules are more volatile and the business environment is more competitive. Sales and operations planning processes must adapt to these new market dynamics.

According to industry analysts, successful projects for S&OP implementation focus more on change management and process alignment than on technology, but information technology (IT) has become an essential element in coping with the ever-increasing complexity of processes and is a vital process enabler for a new approach to S&OP. Recent studies conducted by industry analysts have revealed that initiatives to enhance S&OP can, on average, result in 30 per cent improvement in order fill rates and 25 per cent higher gross margins.³

Clearly, predicting only one possible future is no longer sufficient. Instead, supply chain managers must plan for a range of different scenarios, and this can only be achieved by creating an environment in which the many partners in the supply chain can effectively collaborate to model different possible outcomes and align their activities to accommodate many different supply scenarios. Yet, although collaborative forecasting and planning is at the centre of a company’s demand generation process, a strong strategic trend across large and medium-sized organizations is currently emerging: this aims to anticipate – rather than merely react to – market tendencies and needs, as we can see in the case of a pharmaceutical company with a keen eye on innovation.

CASE STUDY: LABORATOIRES EXPANSCIENCE

French pharmaceutical company Laboratoires Expanscience has built its international business on product innovation, rigorous management and realistic project planning. Its forecasting process has moved from a system-based to a collaborative approach involving its many supply chain partners.

The company’s methodology begins with its estimated sales figures, which are compared to, and matched against, the effective volumes of past sales. The historical data are first cleaned up to remove spikes in volume generated by promotions, stock-outs and seasonal market trends and the resulting data serve as a useful basis for estimating demand generation. Then, at central group level, a system-based algorithm is applied to the data to automatically generate demand forecasts. The process then moves to the level of divisional subsidiaries, where each country manager generates a manual projection supported by a collaborative demand planning suite that is provided by the enterprise software solution. The overall forecast figures are then consolidated from the automated calculation process and the manual adjustments that might be required. Of crucial importance is the way in which the quality of the forecast is measured in order to assess the reliability of the proposed figures for the last quarter against actuals.

This collaborative process allows the company to take a proactive approach to managing the future demand and supply schedules, with planning, rather than mere forecasting, being the predominant activity.

Most pharmaceutical companies still struggle with organizational structures based on silos such as research, development, production and commercial operations. Defining – or redefining – the S&OP process is the best opportunity such companies have to break down the walls between those silos and introduce the concept of the value chain. This is what Laboratoires Expanscience has done. Like many large or medium-sized organizations, it sees the value in collaborative forecasting and planning and is becoming more proactive in anticipating demand and supply schedules to drive the innovation that is vital for its business.

Laboratoires Expanscience’s strategic programme will become its main competitive tool for survival and expansion. To intercept unpredicted events, mitigate disruption and enable management to make better decisions more quickly it has identified the strategic levers in the areas of processes, organization and systems/IT applications that dramatically reduce the need to ‘guesstimate’ sales volumes. The company systematically measures the performance of each supply chain process and has a policy of developing win–win partnerships with customers and suppliers on inventory management by providing more visibility and more accurate forecasting. Planning and operational management are an integral part of this partnership policy. This is giving Laboratoires Expanscience more scope for innovation and, in the cosmetics sector for instance, it is now able to bring new-look products to the market much more frequently than ever before.

At the organizational level the company has implemented a ‘lean office’ project to optimize administrative processes and improve the flow of information, maintaining a continuous link between data on sales and production – two worlds that all too often have conflicting agendas and forecasts.

To further reduce uncertainty around missing predicted future sales volumes Laboratoires Expanscience has created a ‘centre of excellence’ which brings together experts in finance, IT and logistics to support the demand generation activities of the group subsidiaries.

The IT aspect of the company’s programme includes the development of a decision support system that enables rapid feasibility analysis for conflicting scenarios. The results are then shared in a collaborative environment through Web meetings between the subsidiaries and their suppliers using an internally developed system. The company also has its own reporting system at group level that helps to manage growth across its many subsidiary divisions, enabling it to anticipate demand more consistently than would be possible if it relied solely on system-based forecasts.

Agent-based Technology: The New Frontier in Forecasting

As shown by the case study above, technology is a key component of any planning or forecasting process. In many organizations planners rely on rules-based software to support their decision-making process which also draws on their intrinsic knowledge and experience-based calculations. The software follows a formula for the allocation of resources based on predetermined decisions, suggesting that there is a simple process of cause and effect – that is, that the occurrence of event X leads to the probable outcome Y.

Whenever an unanticipated event arises at any stage in the order process, however, it will generate an exception. Many variables – such as...