Comprobación del movimiento de bajada del flotador

What additional benefits and costs are associated with moving

from sharing of downstream sales data to collaborative forecasting in supply chains?

The suggested value of collaborative forecasting is based on the assumption that the quality of the upstream members’ forecasts will be improved through the collaboration. However, Study 4 shows that this assumption does not necessarily hold. In the case examined in Study 4, the retailer’s forecast was systematically poorer than the manufacturer’s, especially for recently introduced products and products approaching the end of their life-cycle. Giving the manufacturer access to the retailer’s forecast would, therefore, not have improved the quality of the manufacturer’s forecast. Study 5, involving a different retailer, provided similar results: two of the development projects examined in Study 5 included retailer involvement in forecasting, however, in neither of these cases did the manufacturer’s forecasting performance improve as a consequence. Study 6 shows that grocery retailers’ lack of forecasting capabilities is a more general phenomenon: most European grocery retailers currently rely on simple time-series forecasts. In such a situation, the added value of manufacturers getting access to retailers’ forecasts is clearly very limited.

The reason why retailers typically have poorer forecasting tools and processes than manufacturers is the difference in retailer and manufacturer forecasting needs. Manufacturers typically need to plan weeks, even months ahead to secure availability of raw materials and packaging materials and to attain efficient capacity utilization in production. Most retailers, on the other hand, have rather limited forecasting needs due to relatively responsive (lead-times of days rather than weeks) and reliable (typical availability levels over 97%) supply chains. This also means that many retailers currently have limited incentives to invest in forecasting collaboration since they do not need to improve their own medium-term, aggregate-level forecasting performance. Although many retailers are developing forecasting capabilities, their main goal is typically to improve short-term, store-level forecasting, rather than aggregate-level, medium-term forecasting, which would be of interest to the manufacturers.

Although it is clear that retailers have limited incentives to share forecasts, this also applies to sharing of sales data, which, too, is likely to benefit the manufacturers more than the retailers. Yet, as shown by Study 6, many retailers share sales data with manufacturers. One reason behind the retailers’ greater willingness to share sales data rather than forecasts seems to be that retailers often share sales data as a part of a replenishment collaboration in which the manufacturer assumes responsibility for

replenishing the retailer’s inventory. The manufacturers, thus, reward collaborative retailers by doing some of the work for them and by guaranteeing product availability, i.e. they provide additional incentives for the retailers to share data. On the contrary, manufacturers to date have not been willing to offer additional incentives, such as improved trade terms or availability guarantees, to retailers who share forecasts.

The retailers’ greater unwillingness to share forecast information compared with sharing of sales data is also explained by the higher costs involved. Several sources have suggested that the required investments in collaboration technology present an important, even the most important obstacle to forecasting collaboration (Fliedner, 2003; McCarthy and Golicic, 2002; Sherman, 1998). Based on Studies 5 and 6, it can be concluded that investment in collaboration technology, such as web-based electronic exchanges, may slow down large-scale collaboration, but that it does not form a critical obstacle to collaboration or a reason for companies not to collaborate. The companies involved in Study 5 considered IT investments to be necessary for scaling up each of the piloted collaboration approaches. Yet, none of the companies considered the required IT investments as significant obstacles to collaboration. In Study 6, it was found that large- scale collaboration is, in fact, possible without significant investments in special collaboration technology. On the other hand, companies have also been able to implement large-scale, more complex collaboration processes using special collaboration technology. On a general level, technology was not given a major role either as an enabler or an inhibitor by the majority of companies interviewed.

In fact, the lack of forecasting tools and resources seems to be the major obstacle to sharing of forecast information, from the retailers’ point of view. Although several retailers are planning to take into use more advanced forecasting tools, few retailers currently have the tools or resources to support sharing of forecasts. In Study 5, the lack of forecasting resources was the main reason why the case retailer was unwilling to scale up one of the piloted collaboration practices. In Study 6, the companies involved in large- scale forecasting collaboration were companies who had already invested in developing forecasting capabilities. Several of the companies currently not involved in forecasting collaboration explained that producing and reviewing forecasts was too laborious compared with the attainable benefits. The willingness to collaborate, thus, seems to be very tightly linked to the retailers’ forecasting capability – if a retailer already has forecasting tools and resources in place, the additional cost of collaborating becomes manageable, similarly to the case of sharing sales data. However, if these components are lacking, the cost of implementing them just for the sake of supporting collaboration is generally considered too high in relation to the attainable benefits.

Sharing of sales data Sharing of system- generated forecasts Sharing of qualitatively adjusted forecasts Collaborative forecasting Information-sharing technology

Data collection and tools for forecasting

Resources for adjusting forecasts

Resources for reviewing exceptions or discussing forecasts Complexity of collaboration T o o l and c a pabi li ty requ irem ent s + + +

Figure 24. Required retailer capabilities, investments and costs related to different levels of information sharing.

Finally, although at this point still a rather tentative finding, it seems that collaboration based on comparing of forecasts may not be a key goal even for the manufacturers. The manufacturers involved in the collaboration projects examined in Study 5 stated that their most important collaboration needs were: 1. To receive binding plans concerning, for example, retailer promotions at an earlier stage, and 2. To get access to more accurate sales data, such as timely POS data for product introductions and historical POS data on promotions, for forecasting purposes. There is, thus, some indication that many manufacturers have seen the CPFR movement as an opportunity to get access to better demand data and increased retailer commitment to plans, rather than to more accurate forecasts, and that this may be the main reason why they have been quick to embrace CPFR although the benefits of comparing forecasts may be marginal.

Though tentative, this finding seems to be supported by the results of the studies presented in this thesis. The results of Studies 1 and 2 indicate that for mature products that are not subject to promotions or seasonality, manufacturer access to distributor sell- through data remedies most of the problems related to demand signal distortion in the supply chain. In these situations, forecasting collaboration is unlikely to bring significant benefits. For products with changing demand, such as recently introduced products, the results of Studies 3 and 4 indicate that the manufacturer can benefit more from access to POS data that enables it to rapidly update its forecasts than from access to retailer forecasts. Finally, in situations of demand peaks that manufacturers need to prepare for in advance, such as promotions, the results of Study 5 show that forecasting collaboration does not necessarily improve forecast accuracy if the retailer lacks the necessary

forecasting capabilities. However, manufacturer access to historical POS data on past promotions in a specific product category may enable improved forecast accuracy.

8 DISCUSSION AND SUGGESTIONS FOR FURTHER