Best Practice: Supplier Report Cards



By Dr. Barry Lawrence, Texas A&M University



Senthil Gunasekaran, Texas A&M University










Pradip Krishnadevarajan, Texas A&M University



Wholesaler-distributors run complicated business models. The intricacies of business relationships combined with significant deployed assets makes for expensive operations. Operations expenses are driven by relationships, however. Suppliers have capacity and customers have needs. It is the mismatch between the two that creates the need for distributors and their capabilities. Most of our blogs to date have dealt with managing the customer relationship and methods to minimize its impact on operations. In this blog post, we address supplier impact and the use of the best practice tool — the supplier report card.

The current No. 1 best-selling NAW Institute for Distribution Excellence book, Optimizing Distributor Profitability: Best Practices to a Stronger Bottom Line (available at http://www.naw.org/optimizdistprof), details best practices, their implementation, and return-on-investment (ROI). These practices are valid in any economy, but the significance of one best practice versus another may change under different market conditions. Each month in this blog, we have introduced a best practice and how it can improve earnings and/or ROI under current economic conditions. We encourage you as you participate in this blog to ask questions, debate results, and offer your own experiences with such practices, so that we may further the knowledge of the community and the understanding of the science of distribution.



The book breaks business processes into seven groups (SOURCE, STOCK, STORE, SELL, SHIP, SUPPLY CHAIN PLANNING and SUPPORT SERVICES) based on various distributor asset categories. This month, we focus on SOURCE as shown in exhibit 1.








Best Practice: Supplier Report Cards

Supplier report cards are not a new concept, but how firms use them often falls short of best practice. In the supplier relationship, the most important criteria are the profitability the products/services provide, as well as the loyalty, performance, and level of services a distributor needs to offer to sell its product.

Exhibit 2 presents a Supplier Stratification model that demonstrates how to evaluate supplier relationships.





The profitability of products/services is a function of the supplier’s quality and innovation combined with the strength of the brand. Supplier loyalty is usually measured by the level of exclusivity the supplier gives to the distributor. If the supplier will sell to anyone and everyone, the competition will drive out all profitability. Distributor services are the levels of support a distributor must provide for less than cost to drive sales of a supplier’s product. Technical support, warranties, repair, and other services often cost more than the customer is willing to pay. Each of the foregoing can be tied directly to ROI, but the numbers are often soft.

Supplier performance, on the other hand, can be directly tied to expenses and asset efficiency (hard numbers). Some key metrics are lead time, variability of that lead time, incomplete orders, quality issues, and other channel specific ones. Performance metrics can usually be captured easily and placed in a supplier report card to assist in process improvement. The practice levels for supplier performance measurement are as follows:

COMMON practice: (1) No defined performance measurement (2) Based on on-time delivery (reactive measurement only) and quality

GOOD practice: Based on a single factor (1) On-time delivery (2) Lead time (3) Quality and delivery completeness

BEST practice: Based on multiple factors (1) On-time delivery (2) Lead time (3) Quality and delivery completeness (4) Lead-time variability (5) Combination methodology-supplier performance index (SPI)

To be clear, a supplier report card is a collaborative effort where a distributor is seeking to create an awareness of improvement opportunities. There are multiple ways a supplier can respond to a report card. One method is to wad it up and throw it away.

A second method, the theoretically correct one, is to proactively use the information to improve its processes for all customers.

A third method is to leave things as they are and make it up to a distributor. Some suppliers will simply make sure the distributor, who is measuring them, gets better treatment at the expense of other customers. Others will compensate the distributor for the cost of covering their failures. These cases often reflect challenges the supplier cannot overcome.

Case Studies

A distributor of automotive parts was having problems with supplier performance. The distributor had the foresight to reach an agreement with its suppliers on the length of lead times and their variability. Each supplier gave a stated lead time plus or minus a certain amount of time (variability). Later, when the distributor started measuring, it discovered safety stocks were running 38% higher than the supplier’s stated lead times should have required.
The distributor issued the report to each supplier. Some of the suppliers improved their performance, but most indicated that foreign demand (remember this was during the massive expansion of China and India demand for cars) was creating significant backlogs. These suppliers offered instead to directly compensate the distributor through rebates until the problems could be resolved.

Many readers may be thinking their suppliers would never do such a thing, and that this distributor must have had so much power that the suppliers were forced into action. This was actually a midsized distributor dealing with large suppliers, however. The suppliers were more concerned about what their performance failures might do to the distributor’s ability to support the end users.

An example that demonstrates how much impact reporting results can have is one about a small fishing distributor. This distributor had annual sales of $3 million and was dealing with a $3 billion supplier. The distributor discovered through inventory stratification that 80% of the supplier’s products were in the “C” and “D” categories. The distributor couldn’t buy in smaller quantities due to minimum order amounts, and line-card restrictions forced it to carry the slow-move items. The distributor decided to drop the supplier.

To the distributor’s surprise, the local supplier sales rep decided to investigate why the line was dropped. When he found out why, he asked for the data and an explanation of ways the supplier could improve. The distributor explained about the line-card rules and minimums. The sales rep took the information back and, through new minimum requirements and a different incentive program, was able to design a process that worked.

In retrospect, the response is not so surprising. This distributor was well known by peers for cutting-edge best practices. The word would have gotten out at the next fishing show and could have damaged the supplier’s reputation.

Bottom Line

The supplier report card adds value through either reduced inventory or other service costs or direct compensation from suppliers. The investment is minimal (make up a report) and so is the risk. Whatever the supplier does will be beneficial. Ever since quality management made its way into the manufacturing environment, suppliers have been using metrics to improve their operations. The supplier report card will be viewed by most as an opportunity. Those who do not will simply ignore it — a pretty good ROI with low risk.

About this Blog

“Managing in an Uncertain Economy” is a blog created by the Council for Research on Distributor Best Practices (CRDBP). The mission of the CRDBP, created by the NAW Institute for Distribution Excellence and the Supply Chain Systems Laboratory at Texas A&M University, is to create competitive advantage for wholesaler-distributors through development of research, tools, and education. CRDBP encourages readers of this blog to send in comments and e-mail this blog to other interested parties.

Best Practice: Setting the Min -- Reorder Point (ROP)



By Dr. Barry Lawrence, Texas A&M University



Senthil Gunasekaran, Texas A&M University










Pradip Krishnadevarajan, Texas A&M University



The most difficult purchasing process to understand is setting the Reorder Point (ROP) also known as the “min.” The ROP will constitute the vast majority of the distributor’s inventory and will determine the most important critical success factor, customer service. Each product has an ROP that is often determined by some form of guesswork. Customer expectations have increased over the past 20 years along two axes: availability and selection. Increased availability means a larger ROP. Increased selection means more ROPs. Both mean more inventory. A larger offering leads to many slow-moving products. Slow-moving products often have ROPs that are very high, compared to sales, which leads to inventory increasing at a faster rate when adding new products.

The current No. 1 best-selling NAW Institute for Distribution Excellence book, Optimizing Distributor Profitability: Best Practices to a Stronger Bottom Line (available at http://www.naw.org/optimizdistprof), details best practices, their implementation, and ROI. These practices are valid in any economy, but the significance of one best practice versus another may change under different market conditions. Each month in this blog, we have introduced a best practice and how it can improve earnings and/or ROI under current economic conditions. We encourage you as you participate in this blog to ask questions, debate results, and offer your own experiences with such practices, so that we may further the knowledge of the community and the understanding of the science of distribution.







The book breaks business processes into seven groups (SOURCE, STOCK, STORE, SELL, SHIP, SUPPLY CHAIN PLANNING and SUPPORT SERVICES) based on various distributor asset categories. This month, we focus on STOCK as shown in exhibit 1.



Best Practice: Setting the Min (ROP)

The ROP is usually determined by one of two methods: an estimate made by a purchasing specialist or by the information system after receiving input from a purchasing specialist. The first process is static in that it does not change over time unless the purchasing person revisits it. In many distributor environments, this is impractical due to the large number of products. The second process is not static, since the system will change the ROP as the forecast and, perhaps, supplier performance changes. Both methods fall far short of what is possible from both a calculation and relationship management best practice standpoint, however.

We will explore the levels of best practice, the value they provide, and what differing members of the organization must do to achieve the best results.

The practice levels for “Setting the Min” are as follows:

COMMON practice: 1) Fixed percentage of safety stock 2) Multiplier set by planner 3) Standard days of supply 4) Service driven (expediting) 5) Static lead time by planner 6) No forecast error in ROP 7) same service level for all items.

GOOD practice: 1) Statistical replenishment 2) Lead time variability measurement 3) Demand variability measurement (forecast error) 4) Service levels driven by inventory stratification.

BEST practice: 1) Dynamic safety stock 2) Actual demand distributions 3) Multi-echelon inventory optimization 4) Service level determined by inventory stratification and financial constraints (service vs. cost matrix).

The common practices have a great deal of guesswork and tend to be slow to respond to market changes. Take for example the popular standard “days of supply.” The firm determines how many days of inventory it wants to carry and sets up rules that may or may not vary by product type or sales. When supply becomes uncertain due to products going on allocation or disruptions in shipping, customer service failures will occur until someone adjusts the rules. If the days of supply requirement was set high due to poor supplier performance or forecasting difficulty, the days of supply may not get adjusted at all when things improve, thus leaving the firm with too much inventory. All of the common practices lack responsiveness, which leads to customer service failures and/or excess inventory (usually both).

Good practice considers changes in the key inventory drivers, lead-time, and forecast performance, and ties the service level to inventory stratification. The ROP moves with changes in the inventory drivers and reduces or increases inventory thereby preventing stockouts while holding down inventory.

Best practice engages more of the firm’s resources to guarantee a high ROI. In addition to changing the ROP as business conditions change and products gain or lose popularity, best practice uses hub-and-spoke techniques to further reduce inventory. Best practice also treats all inventories as an investment that is measured against other opportunities before deployment. The role of ROP in the replenishment process is shown in exhibit 2.




A home products distributor improved its reorder point and resulting inventory levels in stages. At the beginning of the process, the firm had very large inventories with most products carried in all locations. Gross margin return-on-inventory investment (GMROII) for the entire firm was at 170%. The firm was under extreme pressure by ownership to increase its ROI. The company implemented inventory stratification, statistical reorder points, hub-and-spoke, and a new value proposition for the sales force to deliver.

The first step, inventory stratification, involved using a combination process, as described in the Optimizing Distributor Profitability book, to determine inventory status. The inventory management model is shown in exhibit 3.



“D” items were first removed from inventory resulting in an increase in GMROII to 220%. The next step was to improve forecasting and set dynamic ROPs. The process involved combination forecasting, which we’ve described in our previous blog posts. By tying forecasting improvement to a dynamic ROP (one that changes when forecast and supplier performance change), the firm was able to further improve its GMROII to 235% as safety stocks declined. No attempt was made to improve supplier performance.

The next step was multi-echelon inventory management. The firm first went about consolidating “C” item inventory into Regional Distribution Centers (RDCs). “C” items at branch level would be held at the RDC and removed from the branches. This process involved a great deal of inventory and increased GMROII to greater than 280% after implementation. The reduction was driven by the “square root rule of inventory” where inventories combined from multiple locations will reduce to the square root of the number sites the item is taken from times the average inventory per site.

An example: The firm combined three locations’ “C” inventories into one RDC. Each location had an average “C” inventory of $800,000. The new inventory eventually settled at the RDC at $1,385,000 (square root of 3 times $800K) from the original $2,400,000 (three sites at $800K), a reduction of $1,015,000. Hub-and-spoke essentially reduces the ROP to zero at the branch and moves it back to the RDC. The RDC is able to operate with far less inventory by sales volume since the larger volume leads to better forecasting and supplier performance. Safety stock also gets combined allowing for further reductions.

The final step was to develop a value proposition for the sales force. The changes might cause customers to become concerned that service would decline. The firm designed a value proposition built around reinvesting in “A” inventory. The sales force message was: “We are increasing our fill rates on our most important items (examples listed here) that you use the most. To do so, we will consolidate items you rarely or never use (more examples here). I’ll work with you on those items to make sure you can get them quickly when you need.”

The net effect was a reduction on inventory of $25,000,000 even after increasing “A” item inventory. The increase in “A” items more than compensated for the decreased inventory in “D” items. Fill rates increased as a result leading to an increase in sales. The company had calculated its inventory holding cost at 40%, so the overall impact of the program was a $10,000,000 increase in the bottom line. Earnings increased by nearly 80%.

The most important component was the value proposition delivered by the sales force. The firm had to invest in inventory training for salespeople as well as for operations people. Management had to go through a paradigm shift since they had never considered training the sales force on operations issues before.

The process included many tools (forecasting improvement, inventory stratification, value proposition development, etc.), but the key issue was tying all these things to the ROP. Many firms implement improvements without considering this all-important issue. How will it impact the min? Developing a plan for that process is key to all inventory improvement plans.

About this Blog

“Managing in an Uncertain Economy” is a blog created by the Council for Research on Distributor Best Practices (CRDBP). The mission of the CRDBP, created by the NAW Institute for Distribution Excellence and the Supply Chain Systems Laboratory at Texas A&M University, is to create competitive advantage for wholesaler-distributors through development of research, tools, and education. CRDBP encourages readers of this blog to send in comments and e-mail this blog to other interested parties.

Best Practice: Pricing Optimization



By Pradip Krishnadevarajan, Texas A&M University




Senthil Gunasekaran, Texas A&M University











and Dr. Barry Lawrence, Texas A&M University




Companies have been facing gross margin pressure for many years. The real issue is profitability threatened by customer price pressure (the upper boundary of the gross margin) and a declining ability to reduce cost of goods sold further (the lower boundary of the gross margin). Companies have addressed the problem through attempts to decrease the cost–to-serve, thereby increasing the percentage of profit contained within the gross margin. As the gross margin shrinks, the increased profit percentage can “hold the line” on net profits. Unfortunately, the customer has anticipated increased efficiencies and is requiring higher service levels. The result is a constant battle to protect shrinking net profits. In light of these difficulties, many firms have turned to more scientific pricing methods. Pricing is typically market-based, but pricing decisions are very complex and, when made in an information vacuum, will suboptimize gross margins for the firm. The pricing/discounting decision is an information exercise and determines at least half of the firm’s profitability equation.

Most firms have been experiencing a constant gross margin percentage decline during the last four years. The median gross margin percentage for various publicly traded distributors in the distribution space has dropped from 27.2% to 26.5% from 2006 to early 2010 based on data (aggregated by the authors) from a series of reports published by Modern Distribution Magazine (Baird & Co, 2006-2010).

The current No. 1 best-selling NAW Institute for Distribution Excellence book, Optimizing Distributor Profitability: Best Practices to a Stronger Bottom Line (available at http://www.naw.org/optimizdistprof), details best practices, their implementation, and ROI. These practices are valid in any economy, but the significance of one best practice versus another may change under different market conditions. Each month in this blog, we have introduced a best practice and how it can improve earnings and/or ROI under current economic conditions. We encourage you as you participate in this blog to ask questions, debate results, and offer your own experiences with such practices, so that we may further the knowledge of the community and the understanding of the science of distribution.







The book breaks business processes into seven groups (SOURCE, STOCK, STORE, SELL, SHIP, SUPPLY CHAIN PLANNING and SUPPORT SERVICES) based on various distributor asset categories. This month, we focus on SELL as shown in exhibit 1.




Best Practice: Pricing Optimization

Pricing is the gross margin inverse of asset management and procurement procedures. Pricing decisions are very complex. Pricing is typically market-based and, when made in an information vacuum, gross margins can be suboptimized. Since pricing requires basing decisions on a large amount of information, it requires system support. Wholesale distribution pricing has traditionally been approached as an art and not considered a science. Pricing/discounting decisions represent at least half of a distributor’s profitability equation. Pricing decisions on products and services are critical to increasing revenue, profitability, and market share. Applying scientific decision making to pricing is a potential focus area for profitable revenue management.

The practice levels for Pricing Optimization are as follows:

COMMON practice: 1) Cost-plus pricing (2) Cost-plus driven matrix pricing (3) List price or list-less pricing.

GOOD practice: 1) Value-based pricing (2) Pricing matrix based on customer stratification and seller’s item visibility (item stratification).

BEST practice: 1) Pricing optimization models (2) Pricing matrix based on customer stratification, seller’s item visibility, customer’s item visibility, unit cost, and historical gross margins (3) Pricing rules/heuristics.

The most common forms of pricing methods are cost plus, value based, and market-based pricing. In most cases, all three methods are based on a single dimension, which could be cost, value, or competition. The following are characteristics of best practice models:

• They are easy to understand and apply when compared to a complex mathematical environment.
• The core of the model should be based on variables that are typically readily available and/or easily quantifiable.
• They should address data availability and quality issues.
• They should leverage and integrate with existing system processes (for example, inventory stratification).
• You should be able to implement them quickly with minimal IT resources.
• They help achieve incremental business impact (ROI) by using simple pricing models before moving to complex pricing models.

Poor pricing polices and guidelines, a lack of data-driven information, too much speculative information, and the lack of a structured pricing framework will result in margin erosion and pricing inconsistencies. All of these result in the inability to price using item, customer, geography and time combinations. Failure to price effectively results in the following ways:

• Paying customers in services to take products
• Falling into a price lock once companies set an initial price; it is very difficult to increase later
• Losing the opportunity to take advantage of low-visibility and low-sensitivity items that the customer buys infrequently
• Leaving money on the table and maximizing revenue at the expense of profitability.

Distributors often try to increase revenue through discounting to attract more customers. This strategy often backfires and ends up generating lower margins. The pricing optimization framework developed by Texas A&M University’s Supply Chain Systems Laboratory is shown in exhibit 2.

Exhibit 2: Pricing Optimization Framework.

This model is designed for implementation in most IT systems. The model is in no way designed to replace a salesperson or pricing analyst. Instead, the process is designed to provide, in a concise manner, relevant information formatted for use without looking through several different screens in the system and conducting significant offline deduction. The system is the science, and the salesperson brings in the art to make an effective pricing decision. The sales force may feel that they are familiar with whether a customer is profitable or not. However, when you have thousands of products and hundreds of customers, no human being can process that much data. A computer can calculate it in a split second.

Pricing in Action

Weather Supply Company (WSC) is a regional heating, ventilation, and air conditioning (HVAC) distributor that sells in three states with 25 stocking locations in the Southwest. Established in the late 1960s, this company is one of the largest independent wholesaler-distributors in the region. The company sells a full line of HVAC equipment, controls, pipes, valves, and industrial supplies for both commercial and residential construction. WSC provides in-depth industry knowledge to help customers develop comprehensive solutions in a fast-moving, rapidly changing business environment. The 25 branches are grouped under five regions.

WSC was growing rapidly, with multiple acquisitions in the past couple of years. Not only had the distributor’s product lines been growing rapidly, but its customer base has been expanding quickly as well. The growth caused the company to expand its service offerings. The distributor had a high cost-to-serve, and it relied on many suppliers. Management wanted to perform a customer stratification process to determine which customers it could grow with and still stay profitable. They wanted to perform the stratification for the following reasons:

• Pricing decisions. Different customers require different levels of service. Some customers purchase higher quantities, while others purchase lower quantities. These factors contributed to different costs for different customers. The customer stratification process would help WSC understand where each customer stood with respect to cost-to-serve. This stratification would also help in contract bidding processes.
  
• Negotiations. If a salesperson had information about the characteristics of the customer with respect to other customers, he or she would be better positioned to make the right decisions.


• Resource allocation. Sales force and logistics deployment decisions could be made based on customer stratification. For instance, WSC could assign the top salesperson to the most promising customer. The company was performing a great deal of research in lean sales processes. Eliminating nonvalue-added activities from the sales process is the critical success factor to a lean activity. The customer stratification process laid the groundwork for these initiatives.


• Areas of opportunity for future growth. There were areas of opportunity to grow in both customer and product market share. A pricing process driven by the customer stratification process would guide WSC in developing strategies for identifying and capturing those opportunities. It could also provide a roadmap for the marketing department to focus its efforts on these prospects.

WSC decided to apply Texas A&M’s pricing optimization framework. WSC’s analysis was done using a Microsoft Access-Excel custom tool developed especially for this project. The tool was used to demonstrate the methodology and identify pricing opportunities. WSC’s management was ready to implement the practice in their ERP system, but the system could not accommodate certain features of pricing rules and final price calculations. They were able to change the configuration for customer and item analyses, but WSC asked its ERP provider for help in implementing pricing rules. As part of the negotiation, they came up with a creative and collaborative approach to implement the best practice and share the resulting costs. The ERP provider programmed the best practice and the distributor served as a knowledge pilot base for programming and development.

WSC brought its 250 key personnel (branch managers, sales managers, sales executives, and customer service associates) for three one-day education sessions to discuss the methodology and its results using WSC’s data. WSC also unveiled a performance plan that explained how the project’s outcome would be linked to employees’ performance. The plan set the stage for stakeholders to apply what they learned in their day-to-day jobs and provided momentum for sustaining best practices in the future. WSC’s pricing implementation was completed in three months, and the ERP provider’s implementation was completed three months later. WSC’s sales reps applied the pricing tool in their pricing decisions. The six-month journey resulted in improvements to shareholder value. Exhibit 3 shows the margin improvements in a few select locations. There was no drop in sales, since the price increases were conservative. The average GM% increased by 4.3% points.

Exhibit 3: Pricing results.

The following conclusions are drawn from the Pricing Optimization research consortium findings and our experience with hundreds of best practice implementations and educational sessions—which provided the opportunity to observe, interact, and learn from thousands of distribution professionals—over the last 10 years:

• The potential for pricing improvements exists.
• Incorporating both quantitative and qualitative information is a must for successful pricing decisions, but the process should be defined unambiguously.
• Involvement of information technology personnel in pricing implementation (from day one) is paramount.
• Educating the tactical pricing team (inside and outside salespeople, sales and marketing manager, and branch manager) on pricing methodology and getting their buy-in is the single-most important factor in any successful pricing implementation.
• Defining and detailing the roles and responsibilities of various stakeholders involved in the pricing process is a key factor (Who does what and who is accountable for what?).
• Finally, defining quantitative metrics and connecting them to incentive or compensation structure is a vital link in sustaining successful best-practice implementations.

About this Blog

“Managing in an Uncertain Economy” is a blog created by the Council for Research on Distributor Best Practices (CRDBP). The mission of the CRDBP, created by the NAW Institute for Distribution Excellence and the Supply Chain Systems Laboratory at Texas A&M University, is to create competitive advantage for wholesaler-distributors through development of research, tools, and education. CRDBP encourages readers of this blog to send in comments and e-mail this blog to other interested parties.

Best Practice: Forecasting










By Dr. F. Barry Lawrence
Texas A&M University

In the previous blog, we addressed growth and sustainability while maintaining return-on-investment (ROI). Many best practices exist and have been well implemented in information systems to deal with assets and expenses. Increasing revenues is the third part of the equation and the required ROI level is based on the level of risk.

Risk is driven by forecasting. If we miss our forecast on revenues, expenses, or asset needs, we risk not hitting the ROI target. To ensure they meet their ROI requirement, firms will set a higher requirement on activities with a high potential forecast error. The most common cause of misestimating expenses and asset needs, however, is a missed revenue forecast.

The forecasting best practice has advanced dramatically since the introduction of enterprise resource planning (ERP) systems. While the process was hampered by data integrity problems, great strides have been made, and historical forecasting has become more stable. Challenges remain, however, especially where data is scarce.

The current No. 1 best-selling NAW Institute for Distribution Excellence book, Optimizing Distributor Profitability: Best Practices to a Stronger Bottom Line (available at http://www.naw.org/optimizdistprof), details best practices, their implementation, and ROI. These practices are valid in any economy, but the significance of one best practice versus another may change under different market conditions. Each month in this blog, we have introduced a best practice and how it can improve earnings and/or ROI under current economic conditions. We encourage you as you participate in this blog to ask questions, debate results, and offer your own experiences with such practices, so that we may further the knowledge of the community and the understanding of the science of distribution.

The book breaks business processes into seven groups (SOURCE, STOCK, STORE, SELL, SHIP, SUPPLY CHAIN PLANNING, and SUPPORT SERVICES) based on various distributor asset categories. This month, we focus on STOCK as shown in exhibit 1.






Best Practice: Forecasting

Forecasting best practices are divided into three areas: historical (statistical), combination, and collaborative. Commonly used statistical models have been around for many years, and most improvements have been about the forecasting process and not mathematics. Combination forecasting is a rigorous process that can only be conducted on a few key items. Finally, collaborative forecasting requires customer and/or supplier input and is even more work intensive. Human expertise can add great value to forecasting, but it can add bias as well.

The practice levels for Forecasting are as follows:

COMMON practice: A single historical forecast method applied to all products calculated by the system and frequently overridden by purchasing professionals. No use of forecast error metrics.

GOOD practice: Multiple forecasts are run and measured. The best performing forecasting model is selected based on error metrics. Combination forecasting is used where error rates are unacceptably high.

BEST practice: Combination forecasting is applied and augmented by additional information gathered through supply chain alliances with customers and suppliers (collaborative forecasting). Mathematical modeling may be applied through regression techniques.

Mathematical Modeling

ERP systems have allowed for pretty extensive statistical forecasting where data integrity allows. Forecasting models stretching back over 50 years are common in most ERP systems or can easily be accessed from textbooks and put into spreadsheets. These techniques include moving averages, exponential smoothing, models for seasonality and trend, and methods that compare all and choose the best performing one to use on whatever product is being forecast.

The forecast error metrics can measure the average magnitude of error (high or low), the average overall error, and many other views. The error metric (or combination of metrics) that management feels best minimizes the risk of stockout, expenses, or necessary assets (inventory) can then be used to pick the forecast model to trust.

Most distributors do not optimize this mathematical modeling process. They believe that forecasting will never be accurate due to erratic customer behavior, data integrity problems, long supplier lead times, and/or a lack of skills in their purchasing team. They instead let their IT provider set up the system with minimal input. They do not investigate causes of customer ordering patterns, root out data integrity problems, collaborate with suppliers to smooth out lead times, train their purchasing staff, or even try to understand how to measure forecasting and set requirements.

There is no question that even with the best-designed mathematical process, the resulting forecasts will still be highly inaccurate. This is no excuse, however, to not get this phase right. Once the mathematical forecast is properly designed, it acts as the foundation for all that follows. If not properly set up, it will confound all other efforts. Leaving forecasting to external parties (IT consultants) and not training your team is unacceptable.

If the forecast is not trusted, people will deploy inventory to protect against stockouts. Nobody knows the true cost of a stockout, but everyone agrees it far exceeds the cost of inventory. This problem is the number one cause of excess inventories and customer service failures. The distributor has only so many resources it can apply to serve the customer. Ineffective forecasting guarantees there will be too much inventory in some products and customer service failures in others. The role of forecasting is shown in exhibit 2.



Decreasing the Forecasting Burden

If we build the right mathematical process, the next step will be to apply human evaluation to forecasts with excessive error rates. This process is called combination forecasting and has further reduced forecast errors in applications we’ve seen by as much as 50% over mathematical models alone. Effective combination forecasting is a time-consuming process, however.

The steps are simple in concept but very difficult in practice. First, the mathematical model is run to the greatest efficiency possible. Second, the error metrics determine which forecasts are still performing poorly and submit those to purchasing experts to investigate for data integrity problems and trends that are not captured in the data (for example, sharp recessions like 2008, product shortages that will cause long lead times, etc).

Purchasing teams will not have sufficient time to conduct this type of analysis on the 20,000+ products that many distributors carry. To be feasible, the job must be reduced to only the most critical items that require attention. The first step is to use inventory stratification to reduce the purchasing task for both the human and mathematical modeling. “A” and “B” items with high error rates will have to be reviewed. “D” items should be eliminated from inventory and do not need to be forecasted. “C” items, as a previous blog suggested, should only be reordered at the supplier minimums and have little to no reorder point. The correlation between inventory rank and demand stability index (DSI) is shown in exhibit 3.



This process leaves only “A” and “B” items to be forecasted. Since most distributors, who have properly implemented inventory stratification, find more than 70% of their inventory to be “C” or “D,” this process tremendously reduces the forecasting task. “A” items are well behaved and will typically have low error rates. Properly setting inventory stratification is critical, however, to prevent too many items from being put into the “A” and “B” categories. The forecasting framework is shown in exhibit 4.



Forecasting with Unreliable Data

When there is no history or the data is scarce, forecasting becomes extremely difficult. New product introductions, new customers, new territories, and other growth issues covered in May’s blog are very troubling to forecast. If we do not forecast, however, we have no idea what resources will be needed, and we can’t estimate what assets will be used or what expenses will be incurred. We will also not be able to predict what revenues will be produced. This lack of information will produce a high level of risk driving up our ROI requirement, and thereby killing many initiatives.

Forecasting without historical data is difficult but not impossible. The next level of forecasting, collaborative, is rarely used for established products due to its work- intensive nature and alliance problems. Collaborative forecasting is work intensive, since collecting information from customers and suppliers is time consuming and requires human and system handoffs of data that will produce data-integrity issues.

Some large customers have used collaborative forecasting by giving their suppliers the customers’ forecasts and expecting them to follow them. Many distributors report instances where the forecasts were so inaccurate that the motive seemed to be to inflate the distributor’s inventory rather than to make the supply chain more efficient.

Suppliers often have valuable information to share as well. Since a supplier can see how a new product or customer base performs in other regions, they can provide a distributor with valuable information. Many times, however, a supplier, to encourage a distributor to invest, will inflate these numbers.

Other sources can include government-collected data (for example, housing starts, population growth, production numbers, etc.) or data from previous investments of a similar nature.

Collecting these various data sources is work intensive, and the forecasting results may be questionable due to the many potential sources of error. Therefore, collaborative forecasting is rarely used for standard forecasting processes, but is the only alternative when data is not available. Two common ways to calculate a collaborative forecast are human estimation and statistical regression.

Human estimation is most popular, since it is quick and flexible. Contrary to popular belief, it can also be fairly accurate. The key is accountability. When people engage in forecasting, the information delivered to them must be as accurate as possible, and their results must be measured. Measuring human forecast error will create a process whereby the expert will investigate which data gave them the best results and how to improve other sources of data.

Statistical regression is a mathematical process that conducts a very similar analysis. All numerical data (for example, supplier estimates, customer forecasts, similar investment results, government data, etc.) are fed into a mathematical model that will produce a forecast and simultaneously will determine which information sources contributed to forecast accuracy and which did not. Those that did not contribute can be improved for better results or can be eliminated, reducing the data collection workload.

A Cautionary Tale

A building materials distributor developed a complex combination forecasting method. Data was fed into the system and multiple forecasting models were run on the data. The forecasting model that produced the lowest error rate was applied and the best result was continuously measured. Where forecast error on “A” and “B” items was higher than 30% and 40% respectively, the forecast was flagged for purchasing to review. The process dramatically reduced forecast error and inventory at first.

The settings for what qualified as “A” and “B” inventory were too loose, however, and the task soon became overwhelming for purchasing. Purchasing experts were also not trained on how to evaluate forecasts and data. The team began to trust the system even when they should not have, and many high-profile forecasting errors took place ,causing many questions to be raised. It was easier to blame the IT system than to accept blame for the lack of training and a less-than-rigorous human process.

The decision was to discontinue the process and go back to the informal forecasting process. Inventories increased and profitability dropped. New management came in and hired a new set of consultants to improve forecasting. A new IT solution, nearly identical to the previous one, was introduced, again without proper training, settings, measurements, etc. Since many people remembered the signs of failure and lacked confidence in the new application, it took even less time to fail.

A Better Result

A chemical distributor developed a forecasting process that encompassed the sales force, customers, and suppliers. The distributor sold different products in different regions, since they sold primarily to large customers. Each salesperson, therefore, had a few customers buying a very few products.

The distributor had alliances with suppliers where they accounted for more than 80% of the supplier’s production. If the distributor’s forecast was inaccurate, the supplier had little chance of helping the distributor recover. Since the products were very specific, perishable, and could not be acquired elsewhere, it was imperative that the forecasting was accurate.

The sales force was presented every three months with a historical forecast for the products they sold in their region. The forecast was spreadsheet-based, since no information system could support the process that followed. The salesperson was expected to work with customers and reach an agreement on what would be produced and consumed. The salesperson was evaluated and his income was directly tied to the accuracy of the forecast.

Since the customer was consulted and understood the gravity of missing the forecast, forecast error was very low and manufacturing scheduling effective.

Train, Train, and Then Train Some More

Forecasting is one of the most information-intensive processes. While information systems are where it all begins, human interaction is required for critical products and uncertain environments. Well-designed systems, proper collaboration, and accountability, all play their parts, but nothing as complicated as forecasting can succeed without well-trained sales teams and purchasing teams.

About this Blog

“Managing in an Uncertain Economy” is a blog created by the Council for Research on Distributor Best Practices (CRDBP). The mission of the CRDBP, created by the NAW Institute for Distribution Excellence and the Supply Chain Systems Laboratory at Texas A&M University, is to create competitive advantage for wholesaler-distributors through development of research, tools, and education. CRDBP encourages readers of this blog to send in comments and e-mail this blog to other interested parties.

Best Practice: Managing ROI-Driven Growth










By Dr. F. Barry Lawrence
Texas A&M University

So far, so good, most markets are on the mend and wholesaler-distributors are seeing improvements across the board. A clear message came out of the recession: Managing working capital is the short-term goal; managing return-on-investment (ROI) is the long-term one. To do so, distributors must grow profitably. The concept is deceptively simple, however.

Many business people are trying to understand what is being called the “new normal.” The concept is that business conditions will not be the same after the “Great Recession.” They believe that a new model is emerging, and that those who understand and respond to it will flourish. Those who do not will perish. Okay, we’ve heard that before, and anyone who has been in business since the 1990s is probably somewhat sick of such forecasts. They are nearly always wrong.

The trouble is the people making the claim are not just consultants trying to sell their services this time. They are smart business people at best practice firms. They are not publishing their opinions to gain notoriety. They are changing their business models, however.

The new normal is not yet clearly defined, but a couple of common themes are coming from top business thinkers. They involve growing profitably and sustainability.

Growing profitably or, more accurately, with a solid ROI, is a function of four financial inputs:
• Sales

• Expenses

• Assets

• Risk.

The first three constitute the ROI equation; the last one determines the right outcome.

Steps

• The first step is to determine your risk tolerance.

• The second is to determine your potential opportunities.

• The third is to select the opportunities that match your profile of an acceptable risk/return ratio and rank order them to determine which fit within your resource capabilities.

• Fourth, determine what best practices will apply to the venture.

• Fifth, use those projections to govern the project through to a successful and sustainable part of your firm’s portfolio.

Sustainability means the process will continue to operate efficiently. It also means the firm will continuously introduce new innovation to the process to maintain its appeal to customers. These dual objectives require a corporate culture that embraces change, encourages innovation, documents and standardizes processes, and whose human resources are trained and motivated for the task.

The current No. 1 best-selling NAW Institute for Distribution Excellence book, Optimizing Distributor Profitability: Best Practices to a Stronger Bottom Line (available at http://www.naw.org/optimizdistprof), details best practices, their implementation, and ROI. These practices are valid in any economy, but the significance of one best practice versus another may change under different market conditions. Each month in this blog, we have introduced a best practice and how it can improve earnings and/or ROI under current economic conditions. We encourage you as you participate in this blog to ask questions, debate results, and offer your own experiences with such practices, so that we may further the knowledge of the community and the understanding of the science of distribution.

The book breaks business processes into seven groups (SOURCE, STOCK, STORE, SELL, SHIP, SUPPLY CHAIN PLANNING and SUPPORT SERVICES) based on various distributor asset categories. This month, we focus on SUPPLY CHAIN PLANNING as shown in exhibit 1.

Best Practice: Managing ROI-Driven Growth

ROI-driven growth involves identifying opportunities, determining their value, sizing the necessary investment (human as well as financial resources), assessing the risk, and selecting the right options.

The practice levels for ROI-driven growth are as follows:

COMMON practice: (1) Sales force identifies a new application or customer set, (2) Expert (sales) opinion is applied to determine risk and reward, and (3) Selection is made based on biased opinion and force of personality.

GOOD practice: (1) An opportunity assessment is conducted to forecast sales potential, (2) Expenses to support the new opportunity are calculated for varying levels of the forecast, (3) Necessary investments are calculated, (4) ROI is calculated and compared to corporate hurdle rate, and (5) Those activities clearing the hurdle rate are prioritized on perceived risk/return ratio and compared to available resources.

BEST practice: (1) GOOD practice analysis is enhanced with strategy/structure match where the selection is evaluated on its consistency with long-term corporate goals and with existing processes, (2) Sustainability is created by first establishing a measurement system to govern both the implementation and continued process operations and second documenting the process and training to establish a culture of quality and innovation.

The New Normal?

One of the characteristics of the new normal may be shorter, more frequent recessions. JIT (just in time) has reduced supply chain inventories to the point that booms are harder to sustain and recessions cannot last as long. We are still struggling with forecasting this process, but the current recession holds some lessons.

The 2002 recession came as a major shock to most distributors, but they had been working on their inventory processes for quite awhile. Retailers and manufacturers were already running lean on inventory (mostly using the distributor’s inventory), but distributors were still carrying too much. Since they had new inventory processes in place, they adjusted quickly to the downturn. Electronics distributors saw inventory turns drop from 7 to 1 in a matter of months. They stopped ordering and right-sized their inventories quickly, but the impact to manufacturing was horrendous.

2009 brought challenges on a scale not seen since the Great Depression. Distributors were now more agile and manufacturers wasted no time in shutting down operations to control costs in the supply chain. The result was inventory shortages by the summer of 2009, which caused a turn in the economy sooner than most had predicted. Predictions that the economy would have a double dip or a slow climb out were further confounded by the lean distribution inventories. As it sells, it gets ordered, as it gets ordered, it must be produced. When there are any sign that things will soften, manufacturers shut down and distributors stop buying.

Becoming more agile with inventory is a lot easier than closing and opening manufacturing processes, but we should expect manufacturers to make their processes more agile as well. The result could be a new normal of more frequent, shorter, shallower recessions, and less extreme booms. The wild card in this equation is government actions. Programs to overcome the 2002 recession superheated the economy and caused an unwarranted increase in capacity that no doubt contributed to the current recession. Still, whatever the new normal may be, these new, more agile processes make sense.

An interesting outcome of this new normal is that well-run distributors can capitalize on recessions to grow. One distributor described it this way: “When a downturn hits, we reduce inventory and accounts receivables, which increase our available capital. We then use that capital to acquire competitors or launch into new markets when the cost of doing so is very low. Recessions have now become a good thing for us.”

ROI and Growth

A virtuous ROI cycle is illustrated in exhibit 2. A new market opportunity is capitalized on by an acquisition, new product introduction, green field startup, new service model, etc. Sales increase faster than expenses leading to an increase in net margins. If net margins increase at a higher ROI than the firm’s current business processes provide, investors are encouraged to invest more into the new venture.

Another possible model is to improve processes and drive down expenses or assets. The increased ROI allows the firm to decrease pricing or simply capture higher margins. The increase in ROI again encourages investors to put more money into the firm’s improvement efforts.

Exhibit 2: A virtuous ROI cycle.



A Case Study

A small distributor had implemented inventory and accounts receivables management best practices when the recession hit. The distributor continued to manage its assets well and the decrease in inventory together with decreased accounts receivables caused an influx of cash. The firm was very liquid at a time when its competitors were not.

The firm was interested in expanding its territory or service offering. It was not clear which model would work best, so a detailed analysis was conducted using ROI as the driver. The firm was privately owned with a highly competent management team. A new acquisition would be closely supervised, and the firm was experienced in acquisitions. This experience, together with a highly reliable management chain and thorough understanding of the market (the acquisitions would be within 150 miles of their territory), demonstrated a low risk, so the firm set its minimum hurdle rate for ROI at 18%.

Three acquisitions were identified:
• The first was a distributor with an identical business model.

• The second was in a related and complementary product line.

• The third was a service firm that would complement the firm’s existing processes.

Each firm’s sales were tracked and corrected for the recession, and a conservative forecast was put in place for future growth. The forecast was then linked through the income statement of each to determine what their respective expenses would be.

Best practices were compared between the purchasing firm and each acquisition to determine how the merged operation would operate and how any reductions in costs would be captured. This analysis led to estimated net earnings for each of the next five years. The required investment was calculated by a 3X multiple of each firm’s current earnings, plus 75% of accounts receivables and 50% of inventory and adjustments for debt and other assets. The result was a 4- to 5-year payback on each acquisition or an ROI of 20% to 25%.

The firm then assessed the strategy/structure match. The services distributor was determined to be most outside of the firm’s core competency and its low margins were a concern. Service firms have few assets, so while the ROI looked good, the margin for error was small. The distributor with the related product line was less risky, but had a poor record on accounts receivables. The final distributor from the same product/service offering had the overall best ROI, but a large inventory compared to the acquiring firm. The structure was a good match and the proximity of territory meant that assets could be shared, promising opportunities to reduce inventory and other assets (for example, one warehouse was perceived as redundant).

After making the selection, the next step was sustainability. The best practice states that integrating acquisitions should happen as quickly as possible from a human resources standpoint. Delays in eliminating redundancies and creating career advancement opportunities for valued new employees will result in productivity decreases for some and the potential loss of high performers if anxieties are not relieved quickly. High-performing employees are embedded in the actual value of the firm and their loss decreases the value of the acquisition.

The firm treated each asset and new employee as a valued resource and an implementation plan was made to capture the planned ROI with the involvement of key personnel from the acquisition. Training, career path, and involvement were preplanned especially for high-value employees. Transitioning best practices from one firm to the next were planned with metrics to ensure ROI and create sustainability.

Capturing best practices and ROI in standard operations has gone on for some time and is well represented in the Optimizing Distributor Profitability book (http://www.naw.org/optimizdistprof).

Doing the same for growth implies new best practices and new perspectives. If the new normal is as we propose in this blog post, there is a need to manage growth very tightly to maintain the necessary flexibility and capture the opportunities associated with the new business environment. The upcoming Council for Research on Distributor Best Practices (CRDBP) consortium title, “Optimizing Distributor Growth and Market Share” will treat this need as its core mission. The consortium brings together many best-practice companies thereby combining the wisdom of many as opposed to the thoughts of a few. To learn more and to join this consortium, go to http://www.naw.org/crdbp/growth.php.

About this Blog

“Managing in an Uncertain Economy” is a blog created by the Council for Research on Distributor Best Practices (CRDBP). The mission of the CRDBP, created by the NAW Institute for Distribution Excellence and the Supply Chain Systems Laboratory at Texas A&M University, is to create competitive advantage for wholesaler-distributors through development of research, tools, and education. CRDBP encourages readers of this blog to send in comments and e-mail this blog to other interested parties.

Best Practice: Network Optimization









By Dr. F. Barry Lawrence
Texas A&M University

The struggle continues in spite of the light on the horizon. Some are running out of rope, but most have adjusted. One could argue that wholesale distribution was built to overcapacity during the massive expansion associated with the Just in Time (JIT) movement of the 1980s and 1990s and that we’ve been struggling with adjustments in the 2000s. JIT drove a great deal of new business to wholesaler-distributors as manufacturers and retailers sought to decrease inventories, but it also decreased their net margins through an increase in cost to serve.

The initial impact was the 2002 recession, the first indication that the market would not grow continuously. The upturn from 2003 to 2008 gave distributors some relief but most would agree it was not like the 1990s. If distributor growth was not spectacular at that time, we should not have been surprised by how hard the next recession hit. The financial crisis further demonstrated how vulnerable distributors are when our overloaded services (accounts receivables, inventory) strangled cash flow almost instantly.

Going forward, the distributor’s market will grow steadily, but not nearly as rapidly as in the past. For distributors to experience real growth, they will have to provide excellent and innovative services and products. The combination requires best practices in asset management and supplier management. Past blogs have dealt with customer relationships, inventory, transportation, lean processes, and information technology. This blog will address network optimization, the process of aligning all resources in the distributor’s organization to optimize customer service and profitability simultaneously. Network optimization is a very mature best practice but has, to date, not been used extensively by distributors.

The popular-selling NAW Institute for Distribution Excellence book, Optimizing Distributor Profitability (available at http://www.naw.org/optimizdistprof), details best practices, their implementation, and return-on-investment (ROI). These practices are valid in any economy, but the significance of one best practice versus another may change under different market conditions. Each month in this blog, we have introduced a best practice and how it can improve earnings and/or ROI under current economic conditions. We encourage you as you participate in this blog to ask questions, debate results, and offer your own experiences with such practices, so that we may further the knowledge of the community and the understanding of the science of distribution.

The book breaks business processes into seven groups (SOURCE, STOCK, STORE, SELL, SHIP, SUPPLY CHAIN PLANNING, and SUPPORT SERVICES) based on various distributor asset categories. This month, we focus on SUPPLY CHAIN PLANNING as shown in exhibit 1.





Best Practice: Network Optimization

Network optimization involves mathematically modeling the distributor’s operations. The model will include an objective (maximize profitability or minimize cost) and a set of constraints (financial, customer delivery requirements) that may or may not be violated. Management establishes the goals of the firm and the model is based on real conditions. Once the model is created, it can be run repeatedly to determine the optimal configuration of assets in the distributor’s supply chain. Sound difficult? Let’s run through some examples to demonstrate how others have carried out a network optimization.

The practice levels for supply chain network optimization are as follows:

COMMON practice: (1) Experience, (2) Spreadsheet analysis

GOOD practice: (1) Single-asset focused mathematical model (for example, optimizing facility location or transportation)

BEST practice: (1) Multi-asset focused mathematical models (for example, optimizing inventory and transportation together), (2) Stochastic (uncertain) mathematical models

A building materials distributor had acquired one of its largest competitors. The firm’s network went from 50+ to 80+ locations with many redundant facilities. The firm conducted a network optimization with the following parameters:

• Objective: Optimize profitability


• Constraints: Next-day delivery and transportation fleet capacity.

It sounds deceptively simple, but the objective and the constraints are very complex to model. Once modeled, however, the firm was able to run it over and over with different assumptions: changing demand (forecasting), differing levels of transportation capacity (selling or adding trucks), different levels of financial investment (inventory levels), etc. After many runs, a solution began to emerge. The model recommended reducing the number of facilities from 80 to 49. The researchers, however, recommended only eliminating the top 10, since market conditions would change and an operation that was not quite optimal now could be in a few years.

The firm went about consolidating the top recommendations. The most extensive consolidation took place in northern California. Three branches were to be combined into one. The branches were on property valued on the firm’s books at $200,000 each, but could be sold for $1.2 million each (a $3 million windfall). The consolidated center was able to operate on $1.7 million in inventory as opposed to the $3.2 million, which had been scattered through the three branches. The firm had calculated a 40% holding cost, so the bottom line impact for inventory was $600,000 ($1.5 million times 40%), an increase in net margin for the three branches of nearly 100%. Customer service was not compromised, since the model treated it as a “hard” constraint.
Network optimization is to distributors what aggregate planning is to manufacturers. Aggregate planning is the process of determining how manufacturing resources will be used in the coming year. Conducted each year, it ensures that strategic goals for customer service are met with optimal usage of equipment (maximizing ROI). Distributors’ “manufacturing resources” are warehouses, transportation, inventories, accounts receivables, and all other resources engaged in customer service. Network optimization determines how to optimally deploy them. Unfortunately, this process is carried out only by a few distributors and then only after acquisitions or a long period of organic growth that has resulted in an inefficient network of investments.

Best practice states that distributors should conduct network optimization annually or every two years at least. The result would be more efficient use of resources and higher ROI, both necessities in the current market. Growth, in particular, will require higher levels of service or operations in new territories, which will consume additional resources. For most distributors, future investments will require squeezing or at least not wasting someplace else.

Some assume that network optimization is about closing facilities, hub and spoke, or some other consolidation scheme as in the previous example. In fact, it is about the optimal allocation of resources to meet customer needs. Customer requirements still tend to outpace distributor compensation, so the focus is not necessarily about reducing the distributor’s footprint, as it were, but about meeting those requirements as efficiently as possible.

An auto parts distributor was an excellent example of optimizing without closing facilities. The firm served car dealers and had grown rapidly with a highly effective business model. At the time of the network analysis, the firm was delivering twice a day in some markets, daily in others, and every two days in others. Multiple daily deliveries were very expensive in terms of trucking and human resources, so the firm wanted an analysis that would examine new markets for them to grow into and a service versus cost analysis of existing markets to determine if the number of deliveries could be safely reduced in some cases. The analysis showed a need for only one or two new facilities (the present network was very efficient) and made recommendations on service offerings going forward. Sample results can be seen in exhibit 2.

Since network optimization configures the distributor’s service offering, it moves huge resources like facilities, changes customer allocation to operations, determines transportation needs, places inventory and other value processes, and sets customer service levels. The decisions in a network optimization will determine the ability to operate profitably, achieve target ROI, improve cash flow, and optimize growth opportunities.

The process has been and continues to be conducted by best practice wholesaler-distributors, but most distributors are not utilizing network optimization. This lack of adoption puts distributors at risk as suppliers choose their channels to market and customers choose their suppliers. For this reason, network optimization will be further explored to establish more distributor best practices in the upcoming Council for Research on Distributor Best Practices consortium on the topic of “Optimizing Distributor Growth & Market Share.” To learn more and to join this new consortium, go to http://www.naw.org/crdbp/growth.php.

About this Blog

“Managing in an Uncertain Economy” is a blog created by the Council for Research on Distributor Best Practices (CRDBP). The mission of the CRDBP, created by the NAW Institute for Distribution Excellence and the Supply Chain Systems Laboratory at Texas A&M University, is to create competitive advantage for wholesaler-distributors through development of research, tools, and education. CRDBP encourages readers of this blog to send in comments and e-mail this blog to other interested parties.

Best Practice: Technology and Change Management

By Dr. Arunachalam Narayanan, Texas A&M University











and Dr. F. Barry Lawrence, Texas A&M University



Technology implemented and utilized properly is a tremendous asset to any wholesale distribution company. Distributors demonstrate the vast differences in technology adoption indicating many opportunities to implement best practices.

Technology is used for both data capture and systems integration. A company without a good data capture (barcode or RFID) can still utilize technology at the systems integration level (like ERP). The Council of Supply Chain Professionals (CSCMP) conducts an annual survey of supply chain professionals on critical issues pertaining to supply chain. Information technology (IT) and supply chain integration always stand out among the top three challenges for the community. Over the last 3 to 4 years, there has been a shift in the challenge, from just technology and integration to “information leverage.”

This is an important change in perception. In today’s environment, almost all companies have either recently implemented an information system or are in the process of selecting and implementing one. The next step is in understanding how to utilize the system for competitive advantage. Success or failure of the system depends on the ability of the company to utilize technology.

The technology failures of the 1990s are now largely behind us. At that time, firms often reported many disasters and took 3 to 4 years to get their systems functional and 7 to 10 additional years before feeling happy with the change. Recent implementations look a lot better. Distributors adopting new systems now report 2 years of pain and a general satisfaction thereafter. Bolt-ons for significant processes like pricing are now common. In general, the cost of IT adoption or changes has plummeted in recent years. Distributors are giant information machines, so the need to effectively adopt, integrate, and manage systems is not optional, it is the only way to do business.

The popular-selling NAW Institute for Distribution Excellence book, Optimizing Distributor Profitability: Best Practices to a Stronger Bottom Line (available at http://www.naw.org/optimizdistprof), details best practices, their implementation, and return-on-investment (ROI). These practices are valid in any economy, but the significance of one best practice versus another may change under different market conditions. Each month in this blog, we have introduced a best practice and how it can improve earnings and/or ROI under current economic conditions. We encourage you as you participate in this blog to ask questions, debate results, and offer your own experiences with such practices, so that we may further the knowledge of the community and the understanding of the science of distribution.

The book breaks business processes into seven groups (SOURCE, STOCK, STORE, SELL, SHIP, SUPPLY CHAIN PLANNING, and SUPPORT SERVICES) based on various distributor asset categories as shown in exhibit 1. The support services group includes human resource management, finance, and technology. This month, we focus on technology, especially systems integration, which is a process under support services. In future posts, we will explore best practices in data capture.



Best Practice: Information Management

Effective information management requires system integration. System integration is one of the many challenges in the IT domain for regional and national distributors. For firms with an aggressive acquisition growth plan, it is essential to integrate systems across the company to realize potential efficiency targets. System integration enables branches and regions to share key distribution resources, such as inventory. It also enhances operating efficiency and enables the firm to become competitive by reducing the cost to serve. The branches/regions will have better visibility, leading to higher productivity and the ability to share internal best practices through improved communication.

System integration directly affects the critical attribute—data integrity—that affects many financial elements. For instance, data integrity is one of the key reasons for an inventory write-off. At times, data integrity causes the wrong tactical or operational decisions that lead to financial losses in terms of write-off or constrained cash flow.

The practice levels for system integration are as follows:

COMMON practice: Individual legacy systems across regions (with little or minimal integration)
GOOD practice: Partial integration across key distribution functions
BEST practice: Bringing all the systems to one platform after taking care of local challenges

A fencing distributor was an early ERP adopter. By the mid 1990s, its system was running effectively and the company was exploring ways to leverage it further. An opportunity came when the company needed to remove inventory from a branch to improve its ROI. The sales force estimated a 40% decrease in sales, which was very reasonable given that 25% of sales were over the counter (retail), and the company believed another 15% would be lost when those customers stopped using fleet deliveries as well. After removing the inventory, however, the branch had a 25% increase in sales! The reason was that product was now coming from a regional distribution center (RDC) that carried a $7 million inventory compared to the branch’s original $600,000. The most important component, however, was the ability of the branch to see the RDC’s inventory in the fully integrated system.

The key benefits of system integration are

• improved communication
• visibility
• productivity
• asset efficiency
• data integrity.

Many companies use “bolt-on” applications. When acquiring new companies, especially at a rapid pace, it’s easier to keep the systems in place, and not too expensive to have an internal programmer create an interface for the bolt-on system to talk to the main system. While one system obviously can’t do it all, this is not an effective long-term solution. When you have too many different software systems, you are not using the full potential of each one, and you may have to run too many interfaces. You may also run into the problem of updates and resource issues. Consider upgrading your laptop running Windows XP to Windows 7. You have to make sure all the other applications are either reinstalled or updated. The same applies for a company. The only difference between the company’s IT infrastructure and a laptop is that instead of an operating system, it’s your enterprise system and instead of applications, it’s your bolt-on.

In the search for a new information system, due diligence requires forming an internal team and hiring outside experts and consultants. The members of the internal team are crucial in deciding the needs of the new system. The team should consist of employees from all related departments, regardless of whether the department actually uses the system or not. A list of possible software systems for any application in an industry is now easily available thanks to the Internet and industry associations. The Distribution Software Guide is one example and can be found at http://www.software4distributors.com/downloads/2010_Distribution_Software_Guide_Email_Blast.pdf.

Post Installation

The other biggest issue in information management is managing expectations. An IT system is not a “magic pill” to solve the problem. Instead, it’s a tool to help achieve certain goals. An interesting survey in CIO magazine (2003, http://www.cio.com/article/29894/The_Value_of_Enterprise_Systems) demonstrates that the major benefits come to those who wait (exhibit 2).

Exhibit 2.


Our interactions with distributors also reiterate the facts of this survey. Patience is key in an IT system implementation. Projects such as these usually have the tendency to exceed budget and timeline. The most obvious place for cutting corners is training and testing, which is a dangerous game, since training is by far the most important and time-consuming part of any system change.

The training challenge has been played out so many times at so many firms. A roofing distributor illustrates this problem. An early adopter of ERP, this distributor did not allocate enough funding for training. Instead, it went with “train the trainer.” The company sent its most capable specialists in functional areas, who had shown an interest in the system, to training at the IT firm. These individuals would then be expected to come back and train others. The problem was that these people still had a job, would be expected to troubleshoot problems for the company, and also conduct training. It was an impossible workload. Then, of course, these specialists were hired away by consultants because of their new skills.

Change Management

A systems integration project leads to a complex change within a company. Ambrose’s 1987 recipe for successful change (Managing Complex Change Pittsburgh: The Enterprise Grp Ltd.) still holds true today. His recipe for change identifies five critical elements: vision, skills, incentives, resources, and action plan. Lack of even one of these elements may lead to confusion, anxiety, frustration, false starts, or slow change as shown in exhibit 3.



So even after more than 20 years of IT implementation, we can say that the greatest potential is still ahead of us. Whether it’s inventory or customer relationship management, warehousing or sales force effectiveness, transportation or customer service efficiencies, all roads lead to the distributor’s information systems. The IT system will define the relationships and operations of the next generation distributor. The next generation will be automated; connected to customers and suppliers; and every asset, human resource, and customer will be wired in (wirelessly).

About this Blog

“Managing in an Uncertain Economy” is a blog created by the Council for Research on Distributor Best Practices (CRDBP). The mission of the CRDBP, created by the NAW Institute for Distribution Excellence and the Supply Chain Systems Laboratory at Texas A&M University, is to create competitive advantage for wholesaler-distributors through development of research, tools, and education. CRDBP encourages readers of this blog to send in comments and e-mail this blog to other interested parties.