TOCICO

What is the problem? A huge distributor of pharmaceutical goods serves more than 10 thousands retail stores and hospitals as well as 300 of their own retail drug stores is implementing the TOC supply chain solution. It required the company to shift from a push inventory system based on forecasts to pull replenishment based on actual demand. This change required the ability of the company’s distribution center (DC) employees to pick 3 times more lines than ever daily. The DC keeps 8500 SKU. The most obvious and easiest solution is: 1) to increase the DC capital equipment, 2) increase of personnel, 3) increase the number of shifts per day. But such an “easy” solution immediately requires an increase in investment and operational costs. Initially it looks quite difficult taking into account also the fact that ТОС supply chain solution was initiated to increase net profit. The direction of the solution. We’ve assumed in our analysis that current operational processes at the DC were based on local efficiency policies which ate into its capacity. The task then was to change the DC processes in a way that will increase capacity significantly without additional investment and with a minimal increase in operational costs. The DC of a huge distribution company has many undesirable effects (UDEs) like: 1) a huge investment into capacity and equipment, 2) a tendency to focus on an increase efficiency per resource (in this case – per person), 3) interdependencies of operations and high uncertainty. That’s why we decided to apply some approaches and solutions from ТОC make-to-order (МТА) solution in order to gain more capacity. What was our thinking and approach to the analyses? At the start we considered implementing in our own drug store chain only because we earn a higher return on investment there. Traditional push replenishment in the company generated approximately 100 lines per day on the DC. This was the number generated by the drugstores' daily orders. The TOC replenishment requires to replenish daily what was sold yesterday. Thus we get approximately 400 lines per drugstore per day. Such a difference (from 100 lines to 400 lines per drugstore per day) reflects the increase in capacity requirements if we are going to replenish what was sold yesterday what is the impact? It is not 150 stores * 100 lines =15000 pick up lines at the DC under the push system but 150 stores * 400 lines = 60000 pick up lines under the pull replenishment system at the DC. Here we consider only 150 drug stores (not the full 300) as a separate geographical part just for an easy illustration of the logic and calculations. The new solution mechanics. We decided to consider the DC as the system’s capacity constrained resource (CCR). The DC pick area consists of 2 zones: a pallet storage and a conveyor. The pallet storage is designed for pallet stock pick up and stockkeeping of manufacturers' boxes. The conveyor is designed for orders pick up which consists of many SKUs with a relatively low number of items for each SKU line. For instance: one manufacturing box consists of 100 items of a Product X. Meanwhile for each line this is not supposed to be more than 1-5 items. Considering the conveyor more precisely it occurs that the equipment is rather expensive. It’s hard and more expensive to expand in comparison to pallet stock keeping. There are 2 conveyor parameters: how many SKUs can be kept there and how many pick up lines it supports. The conveyor is replenished based on forecasts. Frequently the conveyer stocking policy is violated by stocking excess inventories. For example, the conveyor currently holds a particular SKU with a particular serial number (this applies to pharmaceutical goods) but at the same time the headquarters requires a pick up of another serial number of the same product for some other clients. The conveyor also holds overstocks but additional small portions of goods have arrived then again we put them on conveyor zone. Now, what factors influence the number of pick up lines? For example, the storage of particular SKUs at the conveyor is a factor. In the situation of the “right” SKU location the less time the employee has to reach the particular cell, pick up particular SKU, and put it into the tray impacts employee capacity. This tray next travels by conveyor to another employee in order to be filled in with another SKU. This continues and finally the tray reaches the control point and packaging. Next, we’ve considered the gap in the current measure of cells utilization. As a reason of the Gap is the low number of pick up lines. The conveyor is designed to keep stock for 2 days only. However some SKUs have stock close to zero and we are forced to replenish them urgently or / and the stock is not in a right place on the conveyor. Meanwhile a significant number of SKU has stock for more than 5, 10, 20 and even 30 days. So the gap in pick up capacity was as a result of a gap in product availability that in turn was the result of the stockkeeping policy. This stockkeeping policy was that the SKUs most often demanded are kept in “slowly (remote)” locations (less available) and at the same time SKU with less demand are kept in “locations for high runners” or easily accessible locations. Thus we have identified an UDE that significantly impacts capacity and the next task is to identify the policies which caused the UDE. It’s quite obvious that the replenishment algorithms are the cause. Suppose we have SKUs in manufacturer’s boxes located on the conveyor and the stock is already enough. Also the current rules (which were based on the distribution mentality and practice the headquarters where series of the same SKU orders of drug stores should be picked up). As a result if SKU/series which is kept at conveyor is not the same with SKU/series which was defined for pick up then selected SKU/series should be located on the conveyor. As a result – one more cell is occupied with the same SKU but with different serial number sequences. What was implemented and what is the result. We’ve eliminated this policy – no more manual administration of series of particular SKUs. That alone decreased the number of small good arrivals to the DC. Elimination of only these 2 reasons has increased the number of pick up lines to 70 thousand lines per day. Additional changes were implemented. Unique insight. These changes caused the company such a significant and unexpected increase in capacity at the DC The company was then tempted to increase lines sold into wholesale. It’s the same situation as if one increases the capacity of a very expensive resource which is a CCR and its capacity was increased significantly. This allowed selling products in our other markets at the lower prices also. We presented this problem and recommendation to the company and we made a similar decision. Its truly profitable to convert this additional capacities into additional throughput in our retail market in additon to our drugstore market.