Softworld for the supply chain Supply Chain management

Softworld for the supply chain Supply Chain management

Managing demand flow through a supply chain often represents a complicated and daunting task to most firms. However, it mirrors the old joke about “how do you eat an elephant? … one small piece at a time”. By breaking up the flow into more manageable sections and then synchronising the results, a seemingly impossible task is made feasible. Consequently, Supply Chain Management (SCM) theory is spreading across all industries.

SCM success depends on two critical issues. First, rather than view the supply chain as a series of customer-supplier relationships, participants must view themselves as trading partners, sharing a common goal of satisfying end-user demand by responding quickly at the lowest cost or most profitable option. This is Partner Focused Logistics.

The second factor is the determination of the rate-of-sales (ROS), beginning at the point of consumption.

The objective of SCM is to move the product from “the point of origin to the point of consumption” as quickly and cheaply as possible. Accurate management requires a focus in the chain towards the customer, the customer’s customer or the end user or consumer. Many companies place increased emphasis on using Point Of Sale (POS) data as the basis for their forecast models. However, few companies have successfully integrated an entire supply chain based on POS-driven forecasting and replenishment. A fundamental inhibitor has been the approach companies have taken to forecasting demand at the POS level.

Traditional statistical forecasts which attempt to predict what will be sold within a given period do not normally provide the accuracy needed for proactive logistics optimisation across the supply chain. One basic problem is the high variability at the point of sale due to new products, pricing actions and so on that cause the future to change. The reality is that forecasting what will be sold is not particularly difficult.

Forecasting when and where it will be sold is the issue. Achieving integrated supply chain efficiency begins with a Rate Of Sale Analysis (ROSA). Unless forecasting models understand and consider the rate of product sales through a location, the policies controlling reorder unit size and the factors causing ROS to vary (demographics,promotions and so on), the errors will continue to be relatively high and inconsistent-especially for manufacturers.

Information on changes in demand is normally communicated down the chain as sequential “orders”, allowing only those decisions to be made that are based on existing data and at the time at which the data is received.

These time delays add to operational response and distort the data through amplification. Each part of the chain is essentially working “out of sync” with each other.

Retailers gather information and when they have enough they pass an order to the wholesaler/distributor. The process is repeated between the wholesaler and the manufacturer and the manufacturer and its suppliers.

This sequential processing of information adds distortion and response time to the overall system.

This is well illustrated by “The Beer Game”, a management training exercise developed by Jay Forrester at MIT in 1958, to simulate the flow of a product through a supply chain, via a retailer, wholesaler, distributor and manufacturer.

The product moved from the supply points to the consumer, in response to consumer demand and replenishment orders generated at each level in the chain. The result led to the “Forrester Effect” or acceleration principle, which states that a 10 per cent change in ROS at retail can result in a 40 per cent demand change at the manufacturer. As each participant looked at how off-target their order patterns were over that period, each began blaming the other for their lack of success. But it was the structure of the game that caused the behaviour which in turn caused the players to become “accidental adversaries.”

The same situation exists today. Manufacturers and wholesalers consider retailers’ business practices antagonistic to meeting objectives. Yet the structure of the industry compels them to engage in these practices.

As a result, a retailer may “forward buy” and even be compelled to divert product as conditions warrant. In the retailer’s world, gross margin return on investment (“turn and earn”) is extremely critical. If a manufacturer’s transport-cost objectives require a retailer to purchase in truckload quantities of product, the retailer may not be able to sell that quantity fast enough to meet its inventory – turn objectives and the retailer’s gross margin won’t be able to withstand that infrequency.

The reality is that trading partners can move product at significantly less cost if they all work together. The “root cause” that resulted in the need for forward buying and diversion can be eliminated. However, the entire structure of the relationship between customers, vendors and suppliers has to change. To do so means embracing relationships with potential competitors as well as customers.

Industry must develop relationships inter-organisationally and intra-organisationally that are built on a greater degree of trust and information sharing than currently exists. Once these have been established, it is possible to set cross-functionally optimised goals to unite functions and enterprises, based on common objectives and “most profitable” product flow through the supply chain.

Decisions in the supply chain need to be made at three different levels:

– Strategic – where decisions are made within multiple planning horizons to achieve an enterprise-wide, or even supply chain wide, optimal solution which reflect global objectives, considering the trade offs among functional requirements. High-level decisions such as new product introductions, go/no go decisions on new factories are made. The balance between inventory turn and production capacities can be set and the guidepost for integrated functional objectives can be determined.

– Tactical-planning which reflects the coming days, weeks or months.

Higher-level decisions and cross-functional objectives have already been made but actual demand may deviate from the plan, and more detailed planning within the local problem domain is needed to realign the availability of people, materials and resources to meet the actual demand and bring the operation back within business objectives.

– Operational-typically, this plan reflects a day to two weeks ahead.

It is concerned with the minute-by-minute operation of the plant or distribution centre to ensure that the most profitable way to fulfil actual order requirements is considered and executed.

Obviously, the planning needs and problems to be solved are fundamentally different at each stage and alternative approaches using various technologies are needed. However, these different approaches must eventually complement and integrate with each other and decisions must be communicated or made visible to all planning levels.

Supply chain planning to achieve Synchronous Logistics execution is the process of modelling the different business cycles, costs and constraints at each level and considering the key drivers of demand and replenishment to meet business objectives. It also determines the information requirements, points of interface and systems required to synchronise logistics planning and activities among the different cycles to match product flow to consumption.

In other words, it provides integration, optimisation and “visibility” across the supply chain so that all parts of the chain can see and understand the impact of decisions and demand at any point in the chain … the whole can be greater than the sum of its parts.

The most time-sensitive locations to “out of sync” planning and logistics are manufacturing locations. Without synchronisation to demands from response-sensitive locations higher up the chain, forecasts can be highly speculative and uncertain because of the volatility closer to the point of sale. This is compounded across several hundred or thousands of stock keeping units (SKUs) by location, each with different ROSs, can result in out of stocks, slow-moving and obsolete inventory … that is, high supply chain cost and/or erratic customer service.

Forecasts of demand at the manufacturing level are sensitive to a number of variables, causes and data sources. The result is multiple forecasts being developed by different organisational functions based upon different data sources and, more importantly, driven by different performance measurement requirements. These differences often conflict with other departments measurements.

– Promotional forecasts are measured on units of sales and result from sales analysis, external data, promotions and market research; they are synchronised to the time of sale or promotional event and nearly always deviate from past performance (historical data).

– Operational forecasts are usually derived from statistically based programs using SKU measures analysing historical sales or shipment data sources.

They are synchronised to shipping or production dates or schedule requirements without consideration to the timing of the events causing the order nor the capacities of the locations charged with fulfilling the order.

– Financial forecasts are based on revenue measures (billing or cash receipt date), modelled against financial reporting requirements and are generally aggregated by non-physical reporting structures, for example product, region, country or market.

Synchronous logistics planning requires the integration of all the forecast processes into a single system view with processes to identify discrepancies which can subsequently be reconciled for action. Because of a lack of understanding of the forecast process and synchronisation with manufacturing, vendors and clients often position demand planning applications in competition with one another rather than being co-operative. There is a real need for collaborative decision-support systems.

The issues facing manufacturing are capacity and resource planning.

Given the various production constraints, most manufacturers can’t make a day’s supply of every item each day, even if sales forecasts demand it. Production sequences need to be optimised to generate the optimum use of available resources. The critical success factor is the balancing of demand requirements with the production economies necessary to fulfil demand between production runs, in the most profitable sequence. Traditional, statistical, unlinked and unsynchronised supply chain planning tools and processes don’t provide the accuracy needed to achieve this feat.

Model-based supply-chain planning optimisation technologies are designed to enable synchronous logistics execution to keep production “in sync” with consumer demand and, fuelled by the availability of more robust information, application integration technology and new co-operative trading relationships, will drive supply chain management to the next millennium.

Richard Sherman is senior vice president of worldwide strategic research at Numetrix.

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