In the 1990s 供应链 计划ning was over-hyped. Then it was a new and promising technology category. The ride was wild and wooly. Hype ruled. A decade later, as companies added 计划ners expecting dramatic improvements, the market entered the trough of disillusionment. The promise was not equal to reality.
As a result, software sales slowed. The growth of the 供应链 计划ning technology market disappointed investors resulting in consolidation and the decline of best-of-breed solutions.
In parallel, Enterprise Resource Planning(ERP) providers introduced a new hype cycle. Termed 企业资源计划 II by Gartner, the concept of integrated 计划ning with tightly coupled 企业资源计划 to 先进的计划系统 (APS) was believed to drive higher value. Sadly, it did not. The inverse occurred. In this push, 甲骨文, 树液 and INFOR became revenue market leaders in APS, but under-served the market in the delivery of 革新. Modeling capabilities and usability in these 企业资源计划 solutions had lower business user satisfaction, and required more labor input. This would have been ok if there was value. The sad reality was the escalation of costs and a reduction in the quality of 计划 output. In my opinion, in this journey, industry analysts and consultants were complicit. No one held the technology providers responsible.
Why should you care? Today market turbulence abounds. Spiraling transportation costs, tariff shifts and increased expectations for 客户服务 sparks new interest in 供应链 计划ning. Operations complexity coupled with the rise in demand volatility increases corporate risk. (Reference Figure 1. The larger the bubble, the greater the risk.)
在过去的十年中，多层供应链系统并行发展缓慢，而大多数企业仅在企业系统上进行投资。新数据形式–远程信息处理，GPS，天气，传感器数据–可以提供帮助，但是这些新形式的数据现在不容易适应’s 供应链 solutions. Hence the conundrum for 供应链 leaders of how to build end-to-end 计划ning and evolve 计划ning.
Today, 供应链 计划ning is being redefined. The transition is slow. As shown in Figure 2, fewer and fewer companies are innovators (38%) and the skill level in the consulting market is lower than the past decade.
The drivers? As market demands increase 供应链 leaders want the same level of data usability that they have in their personal lives. New technology capabilities offer promise, but legacy 供应链 计划ning providers are largely putting lipstick on an old pig. More and more business leaders are asking why, “机器学习，认知计算，软件即服务（SAAS），内存计算和更好的数学提供的结合不能提供新的可能性吗？” The leaders do not know the answer, but are looking for solutions. In parallel, the 供应链 计划ning technology leaders assume that they can ask business leaders to define new outcomes. It is a dilemma because they are uncertain. Business leaders are looking to technology providers to bring 革新 to market.
如图3所示，当今最大的问题之一’s 供应链 计划ning systems is production of a feasible 计划. Companies want good 计划s, but they do not know how to define what good looks like.
2）预测增值。 预测增加值（FVA） 是需求管理改进的度量。通过将预测的误差和偏差与朴素的预测相比较，此度量为预测过程增加了纪律性。 （朴素的预测将过去一个月到本月的出货量作为基准。）SAS的软件将此作为标准度量。
3) 可预测性：衡量预测的难易程度。可预测性是对预测中可能发生的情况的分析。像John Galt这样的软件已将此作为标准度量。
4）S&OP Feasibility. S的翻译&OP计划进入执行。 At this time, I have not seen this measurement in existing software. This would be a regular assessment of 计划 adoption into S&OP execution.
5）时间表遵守. The measurement of production 计划ning to actual schedule. This is the most advanced in process industries in the OM Partners software.
6）使用电子表格。 68% of 计划ning happens in spreadsheets. The high use of spreadsheets is an indication of a bad 计划. The complexity of a 供应链 precludes spreadsheet modeling. Work to drive 计划 adoption.
7）招标。接受的负载。 In transportation 计划ning, carriers are assigned by strategic bidding. The loads are usually assigned based on cost. The problem is that the acceptance of lanes by the carriers does not force the carrier to accept the freight when tendered. If the carrier does not accept the freight, the manufacturer loses time in moving the freight and is often forced to go to the open market to source freight. An effective measurement is loads tendered versus loads accepted.
This does not mean that you should not measure typical measurements like error (MAPE, MPE or WMAPE), bias, cost or order fulfillment (on-time and full). Instead, these measurements should be plotted along with these seven parameters of 计划ning to drive continuous improvement. For example, what is the impact of schedule adherence on cost? On 客户服务? What is the impact of FVA on custom service? Inventory levels? Slow and obsolete 库存?