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Theory Of Constraints Theory Of Constraints What is the
Theory Of Constraints
This Scientific principles to manage an enterprise..
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June 2011
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Retailing 360 : A fresh Perspective

Arvind Singh Rana writes about the latest techniques that can facilitate the preservation of product freshness, while in the inventory stage.

With their growing awareness about products in pharma, food and beverage sectors, modern-day consumers are more concerned than ever before about purchasing fresh products. However, the problem of rapidly-dwindling freshness of products as they pass through the supply chain is one that many organisations continue to grapple with. In the March issue of Retail Biz, we had discussed how supply chain issues impact freshness negatively, and why actions taken by most players in the industry are unable to solve the problem. The need to drastically reduce the waiting time a product spends as inventory in the entire supply chain has also been examined. Further, we took a look at how Theory of Constraints approaches the problem. Since time spent as inventory has to come down drastically, we investigated the mechanism of deciding the inventory levels at the various stocking locations in the supply chain.

Through this article, we look into the mechanism of ordering, stock transfer, logistics planning, priority system and so on, as per the Theory of Constraints (TOC) replenishment solution. Towards the end of the article is a case study of one of our implementations, giving an idea about the speed and magnitude of results that can be expected from an effective TOC implementation.

Mechanism for Ordering and Stock Transfer
Most plants produce goods based on a monthly forecast. Inventory is pushed lower into the supply chain, immediately upon production. Thus, the bulk of the inventory lands up at distributors and retailers, where the forecast error is the highest. Plant Warehouses (PWH), which are an aggregation point, have better forecast accuracy on account of the cancellation of positive and negative forecasting errors. Thus, we see that there is a complete mismatch between the forecast accuracy and inventory in the supply chain.

There is a need to change the profile of inventory. The bulk of the inventory should be at the aggregation point, which is the PWH. The plant can now produce for consumption from the plant warehouse. The priority and trigger for production will be based on stock situation at the PWH. We can achieve near 100 percent availability at the PWH with this procedure of production planning and execution (also called the S-DBR or Simplified-Drum Buffer Rope). When availability is very high at the PWH, the inventory required at the carrying and forwarding agents (CFAs) will be equal to consumption in the transportation lead time from the PWH. Based on this logic, inventory norms are defined for all Stock Keeping Unit (SKU)-location combinations.

The objective of the PWH is to fill up the child warehouse as per the norms. Every child shares the status of stocks as compared to the norms for all items with the PWH every day. Thus the order lead time becomes one day as compared to the 15 or 30 day-lead time of the forecast method. The PWH consolidates the requirement for a child warehouse and dispatches as soon as a truck load is formed. Since all the SKUs are available at the PWH, the truck formation time is reduced, and hence, we get a faster replenishment. Although the requirements come in every day, the PWH does not need to dispatch to every child warehouse every day. This guiding principle is called Order Daily, Replenish Frequently. Thus, this mechanism defines how much inventory to be sent and when to be sent.

Now, we need a priority system that guides people clearly in case of conflicts, resource constraints etc. The inventory norm is divided into three equal zones as shown in figure 1. The top zone is green, the middle zone is yellow and at the bottom is the red zone. The colours represent the risk of a stock out. Thus, an SKU in the red zone has a higher risk of getting stocked out as compared to one in the yellow zone. This gives a clear priority system for the PWH; first dispatch the SKUs in red, then those in yellow and if there is space in the truck, send greens as well. Since red indicates the risk of a stock out, it must be expedited throughout the supply chain. Everybody in the supply chain now works on the same priority system. If the item is stocked out, it carries the black colour, and if it is stocked more than norm, it is in white.

The management now has a simple metric to evaluate the performance of the system. There should be zero blacks, zero whites and less than 10 percent reds. The solution ensures near 100 percent availability, with significantly lower inventories and hence higher freshness of products in the supply chain.

Implications on Freshness Levels in the Supply Chain
Consider a supply chain with a single manufacturing facility that caters to country-wide demand. The inventory at every warehouse will be equivalent to the Replenishment Lead Time (RLT) from the source, as shown in the table 1.

The above supply chain delivers 100 percent availability with four weeks of inventory; hence, none of the stocks are older than four weeks. A company also enjoys maximum benefits of economies of scale owing to a single manufacturing plant.

Products with Shorter Shelf Life
The shorter the shelf life, the smaller is the geographical market that can be serviced by a manufacturing plant. For example, a company selling a product with shelf life of 15 days can operate with just four plants in the countryóone for each regionóand reduced transportation batching. Table 2 shows the inventory in the system:

Similarly, for shorter shelf-life products, the TOC replenishment solution works well with reduced geographical scope and increased transportation frequency.

Analysing Feasibility
Logic does not necessarily translate to business results. It is, therefore, very important to substantiate any logical solution with actual implementation experience and business results. Following is a brief case study of our implementation of TOC replenishment solution with a confectionary and beverages major in India (called XYZ Ltd in this article). It has close to 60 SKUs with a statutory shelf life of nine months. There are two manufacturing facilitiesóone catering to all-India confectionary demand, and other meeting the total beverages requirement. The supply chain consists of around 20 Carrying and Forwarding Agents (CFAs) supplying to over 500 distributors across the country. The company used to operate on monthly forecast and products were pushed lower down the supply chain. Every year, XYZ Ltd had to write off expired stocks equivalent to approximately two percent of the revenues. When the stocks aged beyond five to six
months, extensive discounting was employed to offload the inventory to distributors/retailers. In addition, XYZ Ltd suffered significant reductions in sales due to high levels of stockouts in the supply chain. The stockouts in the supply chain were in the range of 40-60 percent.

The engagement between Vector Consulting Group and XYZ Ltd was started with the dual objective of increasing freshness as well as availability in the supply chain.

Phase 1 of the implementation was to attain 100 percent availability at the PWH. Implementation of S-DBR (a TOC Solution for Operations), coupled with the TOC way of managing suppliers, led to near 100 percent availability at the PWH. With the raw material/packing material availability increasing to 100 percent, production lead times have been reduced to half without any increase in setups or process losses. Near 100 percent availability at the PWH acts a decoupling point between manufacturing and the market, thereby ensuring that plant is not exposed to the urgencies. This has led to a capacity release of around 15-20 percent, without any significant capital expenditure.

Phase 2 of the implementation was to move the organisation from a forecast-based to a pull-based supply chain. This involved getting buy-in of CFAs/ distributors, deciding inventory norms, building in supply chain capability to implement ODRF (Order Daily, Replenishment Frequently).

Within six months of our implementation, we have reached freshness levels higher than 98 percent, which means that only two percent of the inventory in the supply chain was older than three months. Thus, the risk of stocks getting stuck in the supply chain for over nine months is near zero. What would appear counterintuitive is that this was achieved along with a subsequent significant jump in availability. The availability at the CFAs increased to over 95 percent, which means on a given day, there was a stockout of only five percent of the SKUs. Add to this, the fact that the inventory in the supply chain was nearly halved, resulting in additional benefits of Capital Release (creating opportunities for range increase by re-investing released capital), First-In-First-Out being followed properly, lower damages in handling and storage etc.

Since, the fundamental conflict between freshness and availability has been resolved; we have reached high level of performance in both the objectives of freshness and availability. This makes results more sustainable, and, at the same time, releases precious management bandwidth (earlier used for follow-ups, dynamic conflicts resolutions etc.) that can now be used for developmental initiatives. The sales team, which used to spend half of its productive time in following up for stocks or answering unhappy customers, can now focus on the activity they were hired for in the first placeósales.

Arvind Singh Rana is Senior Consultant with Vector Consulting Group

 


Source : Retailing 360 « News Highlights


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