Effective Supply Chain Management Strategies – Summary. Conventional wisdom says it takes three to five years and tens of millions of dollars to digitize a company’s supply chain. However, some companies have reaped major benefits, including increased revenue and customer retention, with a faster, more cost-effective approach. This includes collecting available data; use analytics to understand and predict customer and supplier behavior and optimize inventory, production and delivery; and adding automation to renew or introduce processes. The transformation requires three major initiatives: replacing consensus forecasts with a unified view of demand, changing one-size-fits-all supply strategies to segmented ones, and creating a plan to continuously balance supply and demand and manage deviations or disruptions.
Digitizing the company’s supply chain management system is a mega-transformation project that takes three to five years and costs tens of millions of dollars.
Effective Supply Chain Management Strategies
There is an alternative: significant benefits can be derived from a modernization effort that takes 12 to 24 months and costs several million dollars.
The Supply Chain: From Raw Materials To Order Fulfillment
Collection of easily accessible data; use advanced analytics to understand and predict customer and supplier behavior and optimize inventory, production and supply decision making; and adding some automation to update existing processes and introduce new ones.
Most executives believe that digitizing a large company’s supply chain costs tens of millions of dollars. This is expected to be a massive three-to-five-year transformation effort, requiring major investments in cloud technology, installing RFID tags and readers on every product container and in every facility, deploying 3D printing and robotics technologies, and new tools of machines in a workshop to monitor their operation and status. It is believed that everything is needed to break down the walls between functional areas and create an integrated supply chain that provides a competitive advantage.
But in our consulting work for a number of companies, we found an alternative. The experience of these companies, which include a global fashion retailer, a major consumer packaged goods (CPG) manufacturer, a global appliance manufacturer and a high-tech company that makes computers, tablets and workstations, shows that significant benefits are possible than spend several million dollars on supply chain modernization every 12 to 24 months. In these more moderate efforts, companies collect data that is readily available; use advanced analytics to understand and predict customer and supplier behavior; streamlining inventory, production and supply decision-making to reduce costs and improve responsiveness; and add some automation to revamp existing processes and introduce new ones.
The secret to the success of this approach lies in three initiatives: in the first, companies replace consensus forecasts with a unified view of demand. In the second, they are moving away from a one-size-fits-all supply chain strategy to a segmented strategy. In the third, they create a unified plan to continually balance supply and demand and identify and respond to deviations or disruptions.
A Simpler Way To Modernize Your Supply Chain
When implemented correctly, these initiatives lead to lower supply chain costs and higher revenue through lower inventory and improved service levels (the proportion of orders delivered on time and in full). Just as importantly, they allow businesses to increase customer retention. At the fashion retailer, they helped increase market share by more than 28% and double operating profit in just three years. Operating and financial gains from the company’s CPG initiative paid for the costs in just two years. The high-tech company has seen a 10% to 30% improvement in service levels. And the appliance maker realized a 20% increase in revenue, increased the percentage of customers it could provide same-day delivery from 70% to 90%, and reduced operating costs from 3% to 4%.
In this article, we will focus on the CPG manufacturer’s implementation of the approach. This is a particularly instructive case because of the extraordinary challenges the company faced to address the shortcomings of its existing system, which included multiple and time-consuming manual processes, excess inventory, and a large amount of obsolete and damaged products.
The journey begins with rethinking the demand planning process. Traditional approaches use consensus forecasting, where each function (operations, finance, sales, and merchandising (responsible for marketing, promotions, discounts, etc.)) uses standard statistical techniques, historical sales data, and some external data to generate your own forecast. All features are then aggregated and output with a uniform trade-off prediction.
This process has two drawbacks. First, it takes a long time, typically four to five weeks, to generate the various forecasts and reach a consensus that satisfies all business requirements. At this point, the sales data used is old. Second, instead of reconciling data and analysis to produce a single forecast, the people involved typically focus on balancing conflicting forecasts and rely on gut feeling about what drives sales, revenue and margins.
Highlighting Three Critical Levels For Effective Supply Chain Stand Out Supply Chain Strategy
A much better way to generate a unified view of demand is to start with data sets that everyone agrees will provide the most accurate picture. For example, the CPG manufacturer selected four types:
Using this data and advanced analytics, companies can set up an automated five-step circular process that generates supply, financial and business plans for the next 50-80 weeks, the planning horizon for most companies. Here’s what that process looks like for a CPG manufacturer:
First, merchandising planning information (for future promotions, discounts and marketing investments) is combined with consumer, macroeconomic and external data to generate a forecast of market demand by SKU and retailer for each week over the horizon. From our observations, most CPG companies have never attempted to forecast demand at such a granular level.
Second, the demand forecast for each distributor is combined with historical supply data from the company to that distributor to generate a weekly forecast of each distributor’s SKU orders for the horizon.
Manufacturing Supply Chain Study
Third, the company aggregates all order forecasts and turns them into a workable supply plan. The plan considers available resources, including stocks of raw materials and finished products; production capacity constraints; and market objectives (eg, increase sales of a product category in a given retailer-region combination). It also aims to achieve certain performance targets. A CPG company focuses on minimizing total supply chain costs, but the chosen objective will vary from company to company. In some companies, for example, it may be to maximize revenue or the quantity of supplies produced.
The fourth step is to use the weekly SKU supply plan for all retailers to generate revenue and gross margin forecasts at the brand level for each month of the planning horizon.
The fifth step is to compare this financial forecast with the company’s business goals. The difference between the two may prompt a change in your business plan, such as adding more aggressive discounts or investing more in marketing to drive sales.
When considering the adoption of this new process, CPG company managers raised a number of questions that are representative of the type of concerns most executives express about our approach. Let’s go through them one by one.
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Research shows that variability in customer demand is significantly less than variability in retail orders, a reality that underlies the well-known whiplash in supply chains. This means that forecasting consumption should be easier than forecasting retail orders, and in fact the accuracy of a consumer goods company’s forecast of market demand is quite high. At any given time, the five- to eight-week SKU, week and retailer-level demand forecasts proved to be 85 percent accurate.
Combining more accurate consumption forecasts with historical retail orders allowed the consumer goods company to better predict future orders from retailers. The accuracy of the weekly order forecasts was 15 to 20 percentage points better than the standard consensus-based forecasts previously used by the company. Additionally, more accurate order or delivery forecasting clearly translates into a more efficient supply plan that reduces lost sales, thereby increasing revenue and improving service levels and customer experience.
Finally, as the inputs to it are more accurate, so is the financial plan. In multiple implementations of this approach at various CPG companies, the accuracy of financial forecasts made at the beginning of a given month for the following month rose from 95% to 97%.
This question is perhaps the most critical. In fact, in our experience, almost all executives are reluctant to blindly follow the recommendations of a black box developed by data scientists. They rightly want to be able to interpret and explain the outcome of the demand forecasting process.
Purchasing Management: Strategies For Effective Supply Chain Management
For example, is the increase or decrease due to competitor behavior, product cannibalization, promotions and discounts, or simply a special event or holiday? The good news is that today’s analytics technology is mature enough to allow the decomposition of a weekly SKU forecast into its basic components. This is done by explicitly modeling the data as a combination of key variables (competitor behavior, etc.) and estimating the contribution of each to the forecast.
Managers also want to know why, for example, the forecast generated last week is different from the forecast generated this week. This is also information that today’s analytics technology can provide by comparing the inputs used to generate each of the predictions.
Aerial shots of a drink by Bernhard Lang
