Yogi Berra, the great American baseball coach, when pressed on how his team would perform in the coming season, replied, “It’s tough to make predictions, especially about the future.” Berra’s experiences in the challenging world of baseball may have led him to err on the cautious side, but since time immemorial, there have been many pundits and oracles all too willing to take the great leap and offer predictions on our futures. The evidence can be seen all around us with a constant sprinkling of sporting, political, economic and weather forecasts, to name a few. Depending on your point of view, some of these fields of work have in fact delivered greater levels of predictive accuracy as technology continues to advance to better manage and control data. We know all too well the complete and catastrophic failure of our political and financial institutions to predict the recent economic downturn. On the other hand, progress in the accuracy of weather forecasts has been steady as computer power has improved exponentially over recent decades. Generally, however, it remains a minefield. As the management guru Peter Drucker explains, to predict the future is “like trying to drive down a country road at night with no lights while looking out the back window”. That may be so, but performances of business teams in many organisations depends precisely on making accurate predictions and this sometimes governs their very existence. Visualising the future and then providing prescriptions for the public domain can take on many connotations, but the making of forecasts or predictions are actually institutionalised into the way businesses operate and reach decisions. In particular, many organisations rely on the ritual of the annual sales forecast for the coming year to drive plans and budgets on the supply side such as procurement, engineering and human resources. FORECASTING AND SCHEDULING How the sales team derives the figures, the formulas it uses and how it measures their accuracy are topics for regular discussion in board meetings. Forecasts from the customer, economic conditions, product lifecycles and historical trends are just some of the variables thrown into the mix. But, as complexity continues to grow in the global marketplace, the best-laid-schemes oft go awry. Depending on the distance from target can cause major problems in the planning of resources for the coming year. Production schedulers who operate wholly-computer based MRP (manufacturing resource planning) systems to meet sales forecasts know well the problems if they are not converted accurately into solid work orders. The MRP ‘push system’, which is based on the preparation of a multi-period schedule of future demands for the company’s products known as the ‘master production schedule’, uses the computer system to break it down into detailed manufacturing and purchasing schedules. The phrase-push system refers to the mode of planning that these detailed schedules push work onto manufacturing in line with appropriate finish dates and process loading. In this way, it requires manufacturing to produce the required parts and push them onto the next process until they reach final assembly. [caption id="attachment_16151" align="alignright" width="1024"] Figure 1: the scheduler's dilemma (click to enlarge)[/caption] The dilemma (Fig 1) faced by the operational planners who execute the plan is how much of the plan can actually be taken at face value to provide optimum service levels without tying up too much working capital in the process. Their mode of operation is usually based on unsatisfactory and constantly changing compromises, which in particular can be highlighted by how many times the forecasts are updated as the year evolves. The over exuberance of business development teams at the prospect of increased sales can send the wrong signals – the resulting forecasts mean that schedulers, all too aware of the risks, put a greater reliance on increased stock holdings. This results in restricted cash flows and increased operating costs. THREE STEPS TO UNLOCK THE DILEMMA Step 1: Product categorisation – ‘runners’, ‘repeaters’, ‘strangers’ (RRS) and planning mode What, instead, if most of the noise from the external environment could be replaced in part with signals along the supply chain, to instruct each stage with regard to what needs to be produced? The level of risk in solely relying on the forecast as the driver would be lessened considerably, resulting in work orders being derived from consumption rates. The improved accuracy of the system would deliver significant customer service and lower operating cost benefits to the business. [caption id="attachment_16153" align="alignright" width="1024"] Figure 2 (click to enlarge)[/caption] The stalwart tools required to achieve this improved supply-chain performance comes in the form of a combination from Lean and from the Avraham Y. Goldratt Institute’s best practice models. Firstly, the mode of planning can be determined by categorising the products based on existing run rates (Fig 2). An Irish manufacturing example is below. Thought to have originated in Lucas Industries during the late 1980s, the product categorisation into ‘runners’, ‘repeaters’ and ‘strangers’ forms part of an excellent strategy for production scheduling and supply-chain management. When applying the ‘Venetians rule’ (Pareto analysis) to the RRS v current throughput rates, the ‘runners’, ‘products’ or ‘product-family’ – having sufficient volume to justify dedicated facilities or manufacturing cells – make up to ~60% sold, as the example above demonstrates. A ‘repeater’ is a product or product-family with intermediate volume, where dedicated facilities are not justifiable showing a further ~15%. The ‘strangers’ are a product or family with low intermittent volumes making up to ~25% volume, but making up over 70% of the products. Of course, there will be variations across industries, but the principles remain the same – that being classified will determine the optimum scheduling mode of operation (Fig 3). Step 2: Pull for ‘runners’ – push for ‘strangers’ [caption id="attachment_16177" align="alignright" width="800"] Figure 3: Adapted from the Lean tool box, Prof J Bicheno, Lean Enterprise Research Centre, Cardiff Business School (click to enlarge)[/caption] The range of modes to support flow through the supply chain can be seen in Fig 3 and what it demonstrates is that pull systems are more likely with ‘runners’, whilst MRP fits better with ‘strangers’. Pull systems/kanbans control the flow of resources in a production process by replacing only what has been consumed. They are customer order-driven production schedules based on actual demand and consumption, rather than forecasting. Implementing pull systems can help eliminate waste in handling, storing and getting product to the customer on time, therefore improving costs and service levels. This is a critical component of the value stream and the end-to-end supply chain. Step 3: Combine RRS and constraint-management scheduling [caption id="attachment_16171" align="alignright" width="800"] Figure 4: Adapted from TOC Goldratt Institute[/caption] Constraint management (CM) is a philosophy and set of techniques used to manage the throughput of an organisation. Most widely implemented in manufacturing operations, it teaches management how to identify and direct their focus on the few critical drivers.CM begins with one underlying assumption: the performance of the system's constraint will determine the throughput of the entire system. Put simply, the strength of the supply chain is determined at the weakest point, or the pace of the entire supply chain is controlled by the slowest processes. In scheduling terms (Fig 4), it means: 1. Developing a detailed schedule for the constraint resource; 2. Add buffers to protect the throughput of that resource; 3. Synchronise all other resources to the constraint schedule. BUFFER AND SYNCHRONISING TO THE CONSTRAINT The buffer is a period of time to protect the constraint resource from problems that occur upstream. Its effect is to provide a resynchronisation of the work as it flows through the plant. The buffer compensates for process variation, and makes schedules very stable and immune to most problems. It has the additional effect of eliminating the need for 100% accurate data for scheduling. It allows the user to produce a ‘good enough’ schedule that will generate superior results over almost every other scheduling method. After the constraint has been scheduled, material release and shipping are connected to it, using the buffer offset. Material and parts are released at the same rate as the constraint can consume it. Orders are shipped at the rate of constraint production. [caption id="attachment_16170" align="alignright" width="800"] Figure 5 (click to enlarge)[/caption] Of course, implementing the three-step programme will face different organisational, managerial and cultural contexts and the large stock mentality can be hard to break down in some companies. The scheduler’s role changes completely under the new system from the classic expeditor to strategic planning choices for each product classification. Investing in a production, planning and scheduling system that makes to order or stock (depending on the product classification) with a modus operandi along the value stream combined with Goldratt’s very effective ‘theory of constraint’ principles will provide the platform for a winning planning and stock-holding strategy. Furthermore, the inherent risks of being wholly reliant on a sales-forecasting system to determine resources will be greatly reduced.