Implementing Collaborative Forecasting to
Improve Supply Chain Performance


Mr. Saroj Kumar Dash
Skyline Institute of Engineering & Technology
Greater Noida


Collaborative planning and forecasting has been a subject to research in consumer goods industry, and lately related to the utilization of modern ICT technology. Most of the implementations and best practices however are cases between two parties, and the main area of implementation is the retailers. This paper discusses a supply chain of three parties, utilising a collaborative forecasting business model. All the parties of this case study are producing and manufacturing companies, and the different nature of their businesses increases the challenges for collaborative forecasting. The case study concerns extension of a collaborative forecast based business model one step upstream in the supply chain with utilisation of modern ICT technology.

1. Introduction

Until recently the supply chain strategies and their implementation has been concerning one single company or entity. Recently the term integration has been evolving, and the supply chain is seen as a larger group of companies or entities. The integration has first to be performed inside each company or party to enable it to act as one without departmental barriers. To begin with, the supply chain planning processes should be integrated to enable a common view in a form of a common plan. For example, a common forecast is a result of co-operation between sales, marketing, resource planning and purchasing functions. Without a common forecast, the company has no means for successful collaboration with the other parties in the same value chain. Integrating the processes within a company should also cover the service processes, such as order management, invoicing etc. This is where most industries still are; implementing integrated logistics, rather than really managing the supply and demand chain.

The next step from integrated logistics is to involve the suppliers, customers and other intermediate parties of the value chain. It is the linking between enterprises that can lead to the ultimate goal of moving beyond supply chain efficiency to integrating supply with demand.

The shortcoming of supply chain management is that it has focused on efficiencies and execution, operational logistics and manufacturing processes, not so much on improving the competitiveness of a company. The demand chain focuses primarily on revenue enhancement, versus the traditional supply chain emphasis on cost minimization. Second, the supply chain tends to 'push' products based on limited knowledge of the market, versus a 'pull' from the consumers based on current demand. The demand chain is also much more planning and strategy oriented, rather than execution or transactional in nature, since the demand chain uses key consumer and market information that is essential to the strategic planning process The ultimate goal of the demand chain is to satisfy the most profitable markets, while managing service levels for the markets with less profitable demand patterns. Companies will be profitable only if their supply chains are effective, and they will be effective only if they are demand driven

This paper concentrates on the supply chain of the consumer packed goods. The viewpoint is the up-stream of the value chain; the packaging primary material supplier. This entity can also be seen as a second tier supplier to the manufacturer of the consumer packed goods.

2. Supply Chain Management Techniques

2.1. Vendor managed Inventory (VMI)

Vendor Managed Inventory (VMI) is not a new idea this supply chain management technique was established in the mid 1980's. The basic idea of VMI is that the supplier manages the inventory on behalf of the customer including replenishment As the VMI should be beneficial for both parties, some limitations need to be defined:

  • The business relationship between the supplier and the customer has to be established, strong and collaboration oriented, like partnership type for example.
  • Deep trust and extensive sharing of information is required.
  • The material flow should be ongoing (steady, not erratic) and preferably have some historical statistics (realized sales, usage and inventory figures) available.
  • Effective management of VMI increases, if the items or item groups managed are few and substantial of volume. However, with the use of modern information and communication technology (ICT), smaller and numerous items can be managed as well.
  • The VMI setup has to be defined jointly in details. The details include: products (or product groups) included, inventory levels with tolerances, demand (or consumption) levels, demand information sharing rules, transportation routes (modes, lead times, costs etc), warehousing details and exception handling.

In the pure VMI model the customer does not place purchase orders to the seller. The main tool used to operate the VMI is demand estimate or forecast. The customer is responsible for giving the estimate for a period of time and 'use' the goods according to the estimate within agreed tolerances. The customer is invoiced according to the real usage.

When a substantial amount of the supplier's volumes are included in VMI model, it brings benefits for more stable and predictable resource (production, warehousing, transportation etc) planning. It also decreases the Forrester effect; as the safety stock is built purely on known demand, not on assumptions. Especially in industries, which are very capital intensive, the effective and stable load of the machinery creates significant opportunities for increasing profitability. The VMI has potential for increasing customer satisfaction, as it enables the supplier to act beforehand for the high seasons, and thus ensure the delivery reliability.

According to AMR Research, there are significant benefits to be achieved with VMI. When done correctly, some companies have extracted big benefits, such as 50% reduction in lead time, 20% to 70% reduction in inventories or in-stock improvements of 1% to 12%.

2.2 Continuous Replenishment

Another supply chain management method, Continuous Replenishment (CR), emerged in early 1990's. It moves one step further than VMI, so that the visibility to the customer's sales is included. The point of sales (POS) information is used in forecasting, and the forecasting is not purely based on inventory levels. Even though the CR method extends the VMI to cover the inter-company planning; the sales pattern creation is still the weakness of CR.

The 2003 GMA Logistics Study indicates that in the Grocery, Food and CPG sector in the U.S. more than 83 percent of the participants of the survey stated that they have implemented either VMI or Continuous Replenishment (CR) or both. The typical benefits of VMI and/or CR include reduction in inventory, increase in inventory turns, increase in sales and reduction in retail out-of-stocks [3] Berger, R.

3. Forecasting

There are some thumb rules for demand forecasting: first, forecasts are always inaccurate. There is no process that will repeatedly match forecast to actual. Second, quantifying the error and using this to adapt the forecasts is essential to the process. Third, forecasts that are made at higher level (e.g. product families instead of SKUs) will always be more accurate than at the lower levels. Creating forecasts at the lower levels and then grouping them accordingly for planning purposes is vital to a healthy process. Fourth, the planning horizon should be kept relatively short, since errors tend to be significantly fewer in the short term. And, finally, it must be remembered that forecasts are only the starting point for the planning process forecasts help provide a basis for further refinements and the selection of a most likely scenario for the future.

Forecasting and demand estimation in companies usually result to multiple views and forecasts. Integrating the planning processes should lead into one commonly accepted forecast. Best-practice companies have implemented an integrated process, where second-guessing is eliminated. The forecast is made across all the functions resulting to an enterprise-wide forecast. Still today, in most companies each department makes its own forecasts based on multiple information sources. This usually results in excess inventory in process, non-optimal utilization of resources and thus decreases financial results of the company.

In most cases the forecasting and demand estimation is based on historical order or delivery information, which might not reflect the actual demand. However, actual consumer demand may be very different from the order stream. Each member of the supply chain observes the demand patterns of its customers and in turn produces a set of demands on its suppliers. But the decisions made in forecasting, setting inventory targets, lot sizing and purchasing act to transform (or distort) the demand picture.

The further a company is 'upstream' in the supply chain (that is, the further it is from the consumer), the more distorted is the order stream relative to consumer demand. This phenomenon is also known as the Forrester effect or 'bull-whip effect'. It is important to see the meaning of the bull-whip effect both in downstream and upstream of the value chain; the variability caused by the gap (or unbalance) between companies' speculation and postponement of business activities.

This leads to a demand curve with steeper and steeper peaks and downs and with less and less reliability the further up the party is in the value chain. In the upper stream of the value chain the parties are forced to take extreme actions to survive the peaks only to find out that the demand was exaggerated. The total cost of the value chain is increased heavily and the reliability and timelines of the deliveries has suffered.

The cause of the steep demand curve and the fluctuations is not necessarily related to seasonality or economic trend variations. According to Lee et al [6] there are four main causes for the bullwhip effect; demand forecast updating, order batching, price fluctuations and rationing and shortage gaming. The lack of trust for the supplier as well as for the company's internal planning creates these disturbances. Also the fragmented organizations in companies have led to atomistic considerations, i.e. sub-optimization of business activities, which cause the bullwhip effect to occur internally in the company. The multiplied effect of the intra-organizational and cross-enterprise sub-optimization and non-collaborative, non-synchronized, individual processes leads to the bullwhip curve.

The traditional bull-whip definition starts from the basis that each company speculates more in their incoming goods' inventory than in their outgoing goods' inventory. describes a reverse bull-whip effect, where the starting point is the opposite; the company speculates more in the outgoing inventory than in the incoming inventory. If there is a balance between the company's inventory management in incoming and outgoing side, there is no bull-whip effect within that company. In other words that means that the internal forecasting process is operating well, and the company has a common plan or forecast at both ends.

The bullwhip effect can be (at least partially) eliminated through information sharing with suppliers and customers including intra-company suppliers and customers. By sharing information, a common understanding of the real demand can be achieved. Special attention should be paid to finding the items of information causing the overreactions. The final aim is to have centralized demand information one forecast. The four material flow principles, which can be used to reduce the bullwhip effect, are control system, time compression, information transparency and echelon elimination.

4. Collaboration

McCarthy et al [7] define a collaborative supply chain as a long-term relationship among organizations actively working together as one toward common objectives. Another definition of collaboration is that it is 'Eliminating the honest mistakes' [8] Bermudez, J. This refers to that business relations will remain arms length transactions, but the collaboration should target to more visibility and more structured ways of transferring information. The ultimate target of collaboration according to Bermudez [8] should be replacing the inventory with information. The criteria for selecting the collaboration partners are reliability, loyalty and innovation.

In general, managers of companies are far better at the practice of competition than they are at the art of cooperation. Many long-standing barriers exist to thwart successful implementation of collaborative relationships. One major barrier exists incentive systems. While many companies are looking to enhance overall supply chain performance, most company incentive systems still are focused on enterprise or even functional performance.

Issues that earlier were regarded and handled internally, have become more problematic due to the growing fragmentation of providers, accompanied with problems of co-ordination. While outsourcing certain functions, companies also outsource the collaboration from company internal to cross-enterprise level. The fragmentation has also resulted in competitive and individualistic rather than collaborative culture and affected in undermining staff morale and created a climate of mistrust [10] Miller, C. et al.

6. Consumer Packed Goods Industry as the Case

The end products of the consumer goods industry have a large variation; they include products like fresh, dry or frozen food, detergents, cosmetics, pharmaceutical products, etc. The common denominator for all of them is that the consumer creates the principal demand impulses, which reflect the whole value chain.

Collaborative planning has been researched and also implemented in the consumer goods industry, but mainly in the retail area (e.g. Wal-Mart), close to the consumers. The manufacturers, also called as brand owners, are also involved in numerous initiatives, but the suppliers of the manufacturers are still very much an "un-touched" area. The further one goes upstream, the less information of such initiatives can be found. This was also the main reason for this research project, and thus affected heavily on the case study selection process.

7. Consumer Packed Goods Value Chain

The value chain of the CPG sector can be described in many ways there is no one right way to do that. Also it must be remembered that each party in the value chain is belonging to several other value chains, and actually the term chain should be understood as a part of a value network. The figure below describes roughly the parties involved in the CPG value chain.

Figure 6. Parties in the CPG value chain.

The nature, size and power of the parties in the chain vary greatly. The consumer is by far the smallest party, but is the main source for demand information. The major retailers have significant power in controlling the chain (some talk about chain captaincy). The manufacturers can also be global corporations, but the converters are often very small private owned companies. The supplier end of the chain often consists of capital intensive industries such as chemical or paper/board producers.

When looking from the upper stream end, the product changes significantly when it goes down the value chain. Therefore the logistics issues even in the transactional planning cannot be compared to distribution logistics when seen throughout the whole chain.

8. Description of the case study

The case study involves three parties; three individual companies, representing a brand owner (manufacturer), a 1st tier supplier and 2nd tier supplier. The purpose of this selection is to evaluate the potential benefits of an operating collaborative forecasting business model in a three-entity demand chain. The target is to build a lean and transparent business model, where the key are is demand and supply information management and efficient transfer of it.

The evaluation of key improvement processes concentrates on planning and forecasting, and the transfer from traditional order-to-delivery processes. In the traditional process, the purchase order is the key impulse for the supplier, whereas in the new model the key input is the rolling forecast. The challenges of the implementation come from change management, forecasting capabilities, openness and trust. Utilization of modern ICT technology also creates some challenges, but can be regarded as less challenging as the ones mentioned previously.

As stated earlier, there are numerous examples of business models where collaborative forecasting is implemented between two parties. Therefore a selected starting point for this case study is that the collaborative forecasting business model exists already between two parties, and the model is extended one step further. In this particular case, the extension is made towards up-stream, i.e. to the 2nd tier supplier.

As the forecast is the main impulse for the supply chain, the accuracy and relevance of it has significant importance. In a two-entity chain the forecast of the customer affects the supplier. In this case study, where the 2nd tier supplier is included in the same model, the initial forecast from the brand owner affects another step higher in the up-stream. Also the 1st tier supplier's planning process, where the brand owner's forecast is processed into a raw material forecast to the 2nd tier supplier, plays a key role.

A general description of the new business model is described below.

Figure 3. Collaborative forecasting business model between three parties.

9. Starting Point

As the case study involves three companies, it means that the collaborative cross-enterprise forecasting includes two steps; between the manufacturer and the 1st tier supplier, and between the 1st tier supplier and the 2nd tier supplier. The two steps have differences in the forecasting process, but also in issues like location, warehousing, transportation and replenishment mode. The case study will address these issues in the as-is analysis.

Also the production in all three parties involved is different; it varies from process industry to manufacturing. This implies also that the capacity planning and production cycles vary significantly, as well as the life cycle of the products of each party. Process industry is capital intensive, and the profitability comes from capacity utilisation efficiency. In manufacturing, where the production cycles are shorter, the working capital tied in the process has higher impact on the profitability. This results to that the key drivers for effective planning in each party are not the same, and cannot be compared as such.

10. Expected Results and Measuring

At the starting point of the case study, the collaborative forecasting model between the brand owner and the 1st tier supplier was already in place. Therefore it can be stated that the key metrics related to the efficiency of the demand chain are in better status than in the up-stream part of this demand chain. The brand owner 1st tier supplier part is used as a best practice when defining the targets for the 2nd tier supplier 1st tier supplier part. The measuring is done comparing the results of the starting point analysis to a situation where the new business model has been in place for some months.

The new business model is expected to reduce the inventory levels and increase the inventory turnover in the 2nd tier supplier 1st tier supplier part of the demand chain. Other expected benefits are: less out-of-stock situations, less non-optimal transports, better planning possibilities at the 2nd tier supplier resulting in higher production efficiency and increased customer satisfaction due to improved delivery reliability.

In order to obtain these results, a thorough commitment is expected from each partner. In practice it means e.g. implementing new tools to monitor the supply chain, enhanced and even renewed working models as well as structured and defined ways to exchange information. The commitment involves also trust; the new business model must confirm the parties that they can trust both the model and their partners. Without established trust no collaboration will emerge nor survive, and the threat is that each party will again start sub-optimizing their own part of the chain.

At the time of writing this the case study is in progress. After finalizing the implementation of the business model as well as measurements of the key metrics, the final analysis will be made, and results received. The results will provide empirical evidence on the collaborative forecasting model theory. The results can also be used in similar supply chain initiatives as a guide for practical implementation of collaboration.

11. References

[1] Langabeer, J., and Rose, J., Creating Demand Driven Supply Chains, Spiro Press, London, 2001.
[2] Asgekar, V., and Suleski J., Vendor-Managed Inventory: It's Not About Cost, AMR Research Document # 16479, 2003.
[3] Berger, R., GMA Logistics Study.  1-28. 2003.
[4] Gattorna, J. L. (Ed.) , Strategic Supply Chain Alignment. Aldershot, Hampshire: Gower Publishing Ltd, 1998.
[5] Svensson, The bullwhip effect in intra-organizational echelons, International Journal of Physical Distribution & Logistics Management, 33(2), 103-131, 2003.
[6] Lee, H.L., Padmanabhan, V. and Whang, S., Information Distortion in a supply chain; the bullwhip effect, Management Science, 43(4), 415-429, 1997.
[7] McCarthy, T. M. and Golicic, S. L., Implementing Collaborative Forecasting to Improve Supply Chain Performance, International Journal of Physical Distribution and Logistics Management, 32(6),  431-454, 2002.
[8] Bermudez, J., Future Collaboration. AMR Research, 2003.
[9] Bowersox, D. J., Closs, D. J., and Stank, T. P., 2st Century Logistics: Making Supply Chain Integration a Reality, 1999.
[10] Miller, C. and Ahmad, Y., Collaboration and Partnership: An Effective Response to Complexity and Fragmentation or Solution Built on Sand?, International Journal of Sociology and Social Policy, 20(5/6), 2000.

Mr. Saroj Kumar Dash
Skyline Institute of Engineering & Technology
Greater Noida

Source: E-mail May 22, 2006


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