A practical approach to Sales and Operational Planning

This article aims to examine in depth the sales and operational planning process. As in the previous case in which we dealt with the financial planning process, the following is a general introduction to the model, with particular reference to clearly distinct design types and, on request, a series of examples including some practical tips which, although in general, refer to the best practices adopted in real projects.

I refer to S&OP as an integrated planning process of both sales activity (baseline, promotional, discounting, etc.) and the allocation of sales to warehouses and factories, which is generally completed with the determination of the cost of sales and, consequently, the forecasted profitability per customer and product. This type of simulation is a distributed process that requires the interaction of different business areas such as the sales area, trade marketing, supply chain, production and management control. Although the term S&OP is used by various industries, each with its own features and exceptions, I will focus this article on CPG companies or, more generally, Manufacturing.

The main aim of a S&OP model is to balance the market demand with the company’s supply – in terms of purchasing from suppliers, internal production and distribution – avoiding:

  • the risk of stockout when the demand is higher than the production capacity, which may cause delays in delivery of the order and, consequently the request for discounts or the cancellation of the order giving a negative perception of the company;
  • excessive stock levels, which create implicit costs for the company;
  • the use of resources and materials for the production of unprofitable products or products that are not required from the market;
  • a higher cost of transport for the customer due to inaccurate or incorrect allocation of the sales demand to production plants or warehouses (it is preferable, whenever possible, to base one’s choice on the proximity to the customer and other factors which can help reduce the cost of transport).

S&OP processes are, by nature, closely related to businesses and their policies. More or less each company follows a different model and different methodologies to address this issue. These differences may concern: the detail or the timeline extension of the planning, the people involved, the process phases, the use of the bottom-up or top-down approach, etc. For example, if we think about sales planning, this can happen in the case of the aggregation of customers or products, or of the single customer and product, or with a combination of the two methods. It can focus on incomes or on the combination of volumes and prices. In some cases, it can be made centrally by the business management or demand planner and distributed by account managers, or by a combination of these figures.

As a result, it is almost impossible to standardize all cases in a single model. However, we can try to classify the different processes according to the purpose of the planning. We can identify three main typologies:

  • Sales Planning.
  • Sales and Demand Planning.
  • Sales and Operational Planning.

Before entering into the details of each type, I find it necessary to point out that this article covers S&OP as a medium/long-term planning model, even if some authors extend the S&OP process to short-term operational planning, for example the daily scheduling of the production lines.

 

Sales Planning

The Sales Planning model is a centralized process, in which the business management is called to make quantity and value planning for every channel and for an aggregate detail of the size of the customer/product. The Planning activity takes place at institutional moments such as the budget or forecast and the timing detail of the simulation can be annual or monthly

In addition to the gross income planning, the simulation of the following effects is required (however, the managed detail is aggregated):

  • the end-user transport costs
  • variable and fixed sales commission
  • Performance based bonus
  • Business investments

Unless this is a reality with a strong sales uniformity between product and product families and/or customers and aggregate customers, this model coincides with a somewhat simplified view. In some cases, the aggregate data is allocated to customer and product dimensions to then proceed in determining the cost of the sale and the profitability analysis. The allocation driver can be based on historical data or defined by the mix of percentages. In fact, the purpose of a Sales Planning process is to contribute to the enhancement of the finance statement and/or management control of the income statement. There are, however, mixed planning cases where, for example, quantities are defined at an aggregate level while sales lists are loaded for a single product.

 

Sales Demand Planning

The Sales Demand Planning model is typically a rolling quantitative process, distributed to account managers, trade marketing managers, and demand planners. The Planning activity can be manually produced by the responsibilities involved, or pre-initialized through the use of statistical functions or on a historical basis. The objective of this model is to provide, with a broad depth of detail, the quantitative demand for sales divided into baseline and promo curves and to assess the internal availability needed to meet market demands. It is also possible to support the intelligence marketing process through phase-in/phase-out information, market cannibalization, life cycle, the effect of promotional activities on the sales trend, etc.

As already mentioned, the planning detail may be extended to the single product or to a set of main products, although it is still possible to plan on the different aggregation levels. In addition, you may need to decline the information per channel or single customer. Although the simulation interval is not extended (a few months), the scheduling activity takes place monthly and the simulation calendar detail may be weekly or monthly.

The following information is usually required to produce a Sales Demand Planning model:

  • Processed Orders and shipping curves, traced back to simulation detail.
  • The previous year and current year final balance curve, traced back to simulation detail.
  • Trade Promotion planning
  • sell-in e sell-out  statistic curves(Nielsen trends).
  • Sales budget

Theoretically speaking, this type of model would require a rather time-consuming activity each month, and which cannot be allocated to individual account managers. For this reason, at the very beginning a baseline curve is often developed, with the help of various statistical algorithms. The demand planner, responsible for this activity, will therefore be able to decide the best result for each product by using, also in this case, statistical algorithms (best-fit algorithms). Subsequently, the sales force may work exceptionally with targeted manual adjustments, only for certain items or in the case of the combinations of the most important items/channels/customers. Finally, trade marketing managers are called upon to complete the planning data with information about the promotional campaigns.

 

Finally, the demand planner is able to allocate the demand planning – the baseline plus promos – to the warehouses or production plants, preferably basing it on the shipping customer, following an analysis of their availability.

 

Sales and Operational Planning

The Sales and Operational Planning model is a forecasting distribution process, in which sales and promotional campaigns are organized in quantity and value and has been created for account managers and trade marketing managers and for each client/product at a both aggregate level and in detail. Furthermore, it combines operational and finance activities, in order to optimize the distribution chain and its associated costs. In particular:

Operative results

  • Sales demand planning – baseline plus promotional campaigns
  • Allocation of the sales demand to production plants and/or warehouse
  • Use of the MRP explosion to determine Internal Production, Contract Work and Purchase Costs of All Distinct Materials, whether they are finished products, semi-finished products or raw materials.
  • Warehouse simulation via the objective and/or security stock curves
  • Analysis of production line capacity and demand redistribution in case of saturation of demand.

Finance results

  • Planning of purchase costs per item and, in some cases, supplier and simulation period.
  • Calculation of man and/or machine rates based on forecasted economic data, detailed by cost center.
  • Calculation of the cost of sales and valuation of the forecasted stock.
  • Income statement for each customer and product.
  • Forecasted profitability analysis for customer and product.
  • Analysis of the variations between the forecast and final balance for the effects on the purchase price, the production or purchase quantity, mix on the family product, exchange rate differences, etc.

Strategy results

  • Simulation of the profitability of a new product
  • Simulation of the use of Bill of material (BOM) or alternate working cycles.
  • Simulation of new production lines.

 

Unlike the previous case, the latter process is typically executed at institutional times such as the budget and the forecast. The difficulty of such a structured model is due to the closely linked interaction between the various corporate figures. In fact, a distributed process with well-defined levels of approval, dead line delivery, and tight relationships between interconnected activities must be structured so that any variations, such as in the quantities sold , can be easily trackable and manageable at a following stage – allocation of production plants.

In one of the next articles we will deal in detail with production and purchasing management both in operational terms, as a medium/long term planning and analysis model, and finance, as cost calculation.

 

Michele Barsanti

Director International Team – AKC

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