How to avoid placing goods on the shelves in the warehouse?
Each producer forecasts his sales, and thus production. How to do it more accurately, keeping consistent information in the customer service and planning process? How to avoid placing goods on the shelves in the warehouse? How to introduce new products to the market, adequately forecasting the demand?
What is the problem, or invisible demand?
How to optimally forecast the sale of products – for example cosmetics or groceries – so that they do not linger in warehouses, understating sales and profits? How to properly plan your production to include marketing assumptions about introducing new or changing existing products? These are the questions that all manufacturers of this type of products are looking for. A very important element of such action is proper measurement and planning of demand for their products, and then preparation of production. It should be foreseen what and at what time there will be demand from customers visiting stores (both in traditional and electronic channels), and thus distributors and shopping centers. When planning the level of orders, manufacturers must take into account many circumstances and factors such as the seasonality of its offer, its diversity, new products introduced on the market and expiration dates. Currently, companies are dealing with this issue, using forecasting models that omit many reasons for remaining goods on storage shelves. One of them is quite often passing by the product offer presented by producers with the expectations of customers. It happens that people responsible for sales in the company – traders – can not offer buyers exactly the product that the customers count on. In this situation, the bidder’s company statistics shows that the product has “not sold”, but there is no information for why it happened. Currently, companies are dealing with this issue, using forecasting models that omit many reasons for remaining goods on storage shelves. One of them is quite often passing by the product offer presented by producers with the expectations of customers. It happens that people responsible for sales in the company – traders – can not offer buyers exactly the product that the customers count on. In this situation, the bidder’s company statistics shows that the product has “not sold”, but there is no information for why it happened. Currently, companies are dealing with this issue, using forecasting models that omit many reasons for remaining goods on storage shelves. One of them is quite often passing by the product offer presented by producers with the expectations of customers. It happens that people responsible for sales in the company – traders – can not offer buyers exactly the product that the customers count on. In this situation, the bidder’s company statistics shows that the product has “not sold”, but there is no information for why it happened. It happens that people responsible for sales in the company – traders – can not offer buyers exactly the product that the customers count on. In this situation, the bidder’s company statistics shows that the product has “not sold”, but there is no information for why it happened. It happens that people responsible for sales in the company – traders – can not offer buyers exactly the product that the customers count on. In this situation, the bidder’s company statistics shows that the product has “not sold”, but there is no information for why it happened.
– Sales are important in business and therefore you should use all available technologies that support this sale – says Jacek Rakoczy, co-creator of Bpower2 software – unfortunately current sales forecasting systems and models do not contain or contain but only rudimentary information about what I would call unrealized demand. The point is that when a customer wants something, and it does not, this information must not be omitted, because it distorts the real market demand for specific products and distorts forecasts of production and sales – says Jacek Rakoczy.
It may happen that we do not have information about the unrealized demand at all, because the order for another (available) product has been changed immediately. In this way, the demand for these or other articles virtually disappears from the forecasting model of the company and is not taken into account in subsequent sales plans. This is the reason for inaccurate planning of demand and then production, which leads to storage of goods in warehouses.
How does it look in practice?
People responsible for shopping in a given company have the task of buying goods that their customers need (clients). When the bidder’s warehouse does not have what they want to buy, they receive a kind of substitute offer. Sometimes it is a commodity that is to be withdrawn from production. Sellers from a company that offers their products, who work with contracting people, have the goal to sell what is in stock, and on the other hand to launch new products on the market. In the event that they can not meet the expectations of the contracting parties, they convince them that the products offered are very similar and equally good, and sometimes also cheaper (because they are withdrawn from sale). As a result of this type of negotiations, the buyers buy products that have been offered to them. They place an order for an offer they did not originally intend to purchase. They focus on satisfying their needs in terms of adequate stocking. At the same time, they care about the appropriate results of their own sales.
In a situation where a given commodity is ordered at a particular time and it turns out that it is not available then (especially in the FMCG sector), the ordering people decide to replace it. A product that usually does not fully meet the expectations set by them.
The forecasting model gets information about what products have been sold and a simple model makes decisions to produce more of this product. The system “loses” information about what the buyer really wanted to buy, leaving only a trace of what he actually bought. As a result, the planning error, instead of decreasing, increased. The demand that has been made (orders accepted and delivered) is subject to a detailed analysis, but the part that has not been executed is usually not subject to any further procedure. This is because in the sales systems there is no information about the reason for this type of situation. The actual demand and the reason for its non-implementation were not taken into account.
As a consequence, subsequent forecasting cycles take into account erroneous data regarding the number of products sold. Those that were de facto ordered due to the unavailability of other articles. In turn, products that have not been sold and which are often not in orders are not taken into account when forecasting.
– The goal is not to “push” the recipients of what is just on the shelf in the warehouse, but to offer what they really need – says Jacek Rakoczy, CEO of Bpower2 – if the product is not present today, it is worth noting that customers ask about and when such information goes to the forecasting system, and then it will be properly processed and interpreted, it will suddenly turn out that the sale grows, because the recipients receive exactly what they want – adds Maciej Pluta.
Current statistical models very often do not have such data or these data are inadequate due to lack of process approach to the whole issue. They do not provide unrealized demand in an appropriate way to forecasting systems (along with information about the reason for not meeting demand). This may result, for example, in inaccurate estimates of the number of new products being introduced to the market and artificially inflated forecasting of withdrawn products.
Solution...
If you have met with such a situation and want to fix it, you need something more than known and currently used tools. It is important to capture the actual demand, even if it is not realized. It is necessary to prepare product lists together with quantities that have not been sold and the reasons for rejection. Such data must be processed, which means that it should be analyzed by the planning team. Some of them may turn out to be real demand, and some may be normal mistakes or extraordinary phenomena that should not be taken into account in demand and sales forecasts. Without proper system support, the implementation of such a task is very difficult. Therefore, you must use the automation of business processes that will help you generate and process information and ML modules,
Jacek Rakoczy – MBA, president of the board and originator of Bpower2. He has over twenty years of experience in the IT sector, specialist in ERP systems (CSBI, QAD, IFS, SAP).